data visualization

Plots in data science play a pivotal role in unraveling complex insights from data. They serve as a bridge between raw numbers and actionable insights, aiding in the understanding and interpretation of datasets. Learn about 33 tools to visualize data with this blog 

In this blog post, we will delve into some of the most important plots and concepts that are indispensable for any data scientist. 

1. KS Plot (Kolmogorov-Smirnov Plot):

The KS Plot is a powerful tool for comparing two probability distributions. It measures the maximum vertical distance between the cumulative distribution functions (CDFs) of two datasets. This plot is particularly useful for tasks like hypothesis testing, anomaly detection, and model evaluation.

Suppose you are a data scientist working for an e-commerce company. You want to compare the distribution of purchase amounts for two different marketing campaigns. By using a KS Plot, you can visually assess if there’s a significant difference in the distributions. This insight can guide future marketing strategies.

2. SHAP Plot:

SHAP plots offer an in-depth understanding of the importance of features in a predictive model. They provide a comprehensive view of how each feature contributes to the model’s output for a specific prediction. SHAP values help answer questions like, “Which features influence the prediction the most?”

Imagine you’re working on a loan approval model for a bank. You use a SHAP plot to explain to stakeholders why a certain applicant’s loan was approved or denied. The plot highlights the contribution of each feature (e.g., credit score, income) in the decision, providing transparency and aiding in compliance.

3. QQ plot:

The QQ plot is a visual tool for comparing two probability distributions. It plots the quantiles of the two distributions against each other, helping to assess whether they follow the same distribution. This is especially valuable in identifying deviations from normality.

In a medical study, you want to check if a new drug’s effect on blood pressure follows a normal distribution. Using a QQ Plot, you compare the observed distribution of blood pressure readings post-treatment with an expected normal distribution. This helps in assessing the drug’s effectiveness. 

4. Cumulative explained variance plot:

In the context of Principal Component Analysis (PCA), this plot showcases the cumulative proportion of variance explained by each principal component. It aids in understanding how many principal components are required to retain a certain percentage of the total variance in the dataset.

Let’s say you’re working on a face recognition system using PCA. The cumulative explained variance plot helps you decide how many principal components to retain to achieve a desired level of image reconstruction accuracy while minimizing computational resources. 

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5. Gini Impurity vs. Entropy:

These plots are critical in the field of decision trees and ensemble learning. They depict the impurity measures at different decision points. Gini impurity is faster to compute, while entropy provides a more balanced split. The choice between the two depends on the specific use case.

Suppose you’re building a decision tree to classify customer feedback as positive or negative. By comparing Gini impurity and entropy at different decision nodes, you can decide which impurity measure leads to a more effective splitting strategy for creating meaningful leaf nodes.

6. Bias-Variance tradeoff:

Understanding the tradeoff between bias and variance is fundamental in machine learning. This concept is often visualized as a curve, showing how the total error of a model is influenced by its bias and variance. Striking the right balance is crucial for building models that generalize well.

Imagine you’re training a model to predict housing prices. If you choose a complex model (e.g., deep neural network) with many parameters, it might overfit the training data (high variance). On the other hand, if you choose a simple model (e.g., linear regression), it might underfit (high bias). Understanding this tradeoff helps in model selection. 

7. ROC curve:

The ROC curve is a staple in binary classification tasks. It illustrates the tradeoff between the true positive rate (sensitivity) and false positive rate (1 – specificity) for different threshold values. The area under the ROC curve (AUC-ROC) quantifies the model’s performance.

In a medical context, you’re developing a model to detect a rare disease. The ROC curve helps you choose an appropriate threshold for classifying individuals as positive or negative for the disease. This decision is crucial as false positives and false negatives can have significant consequences. 

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8. Precision-Recall curve:

Especially useful when dealing with imbalanced datasets, the precision-recall curve showcases the tradeoff between precision and recall for different threshold values. It provides insights into a model’s performance, particularly in scenarios where false positives are costly.

Let’s say you’re working on a fraud detection system for a bank. In this scenario, correctly identifying fraudulent transactions (high recall) is more critical than minimizing false alarms (low precision). A precision-recall curve helps you find the right balance.

9. Elbow curve:

In unsupervised learning, particularly clustering, the elbow curve aids in determining the optimal number of clusters for a dataset. It plots the variance explained as a function of the number of clusters. The “elbow point” is a good indicator of the ideal cluster count.

You’re tasked with clustering customer data for a marketing campaign. By using an elbow curve, you can determine the optimal number of customer segments. This insight informs personalized marketing strategies and improves customer engagement. 

Improvise your models today with plots in data science! 

These plots in data science are the backbone of your data. Incorporating them into your analytical toolkit will empower you to extract meaningful insights, build robust models, and make informed decisions from your data. Remember, visualizations are not just pretty pictures; they are powerful tools for understanding the underlying stories within your data. 

Check out this crash course in data visualization, it will help you gain great insights so that you become a data visualization pro: 

Heatmaps are a type of data visualization that uses color to represent data values. For the unversed,
data visualization is the process of representing data in a visual format. This can be done through charts, graphs, maps, and other visual representations.

What are heatmaps?

A heatmap is a graphical representation of data in which values are represented as colors on a two-dimensional plane. Typically, heatmaps are used to visualize data in a way that makes it easy to identify patterns and trends.  

Heatmaps are often used in fields such as data analysis, biology, and finance. In data analysis, heatmaps are used to visualize patterns in large datasets, such as website traffic or user behavior.

In biology, heatmaps are used to visualize gene expression data or protein-protein interaction networks. In finance, heatmaps are used to visualize stock market trends and performance.  This diagram shows a random 10×10 heatmap using `NumPy` and `Matplotlib`.   

Advantages of heatmaps

1. Visual representation: Heatmaps provide an easily understandable visual representation of data, enabling quick interpretation of patterns and trends through color-coded values.
2. Large data visualization: They excel at visualizing large datasets, simplifying complex information and facilitating analysis.
3. Comparative analysis: They allow for easy comparison of different data sets, highlighting differences and similarities between, for example, website traffic across pages or time periods.
4. Customizability: They can be tailored to emphasize specific values or ranges, enabling focused examination of critical information.
5. User-friendly: They are intuitive and accessible, making them valuable across various fields, from scientific research to business analytics.
6. Interactivity: Interactive features like zooming, hover-over details, and data filtering enhance the usability of heatmaps.
7. Effective communication: They offer a concise and clear means of presenting complex information, enabling effective communication of insights to stakeholders.

Creating heatmaps using “Matplotlib”   

We can create heatmaps using Matplotlib by following the aforementioned steps:  

• To begin, we import the necessary libraries, namely Matplotlib and NumPy.
• Following that, we define our data as a 3×3 NumPy array.
• Afterward, we utilize Matplotlib’s imshow function to create a heatmap, specifying the color map as ‘coolwarm’.
• To enhance the visualization, we incorporate a color bar by employing Matplotlib’s colorbar function.
• Subsequently, we set the title and axis labels using Matplotlib’s set_title, set_xlabel, and set_ylabel functions.
• Lastly, we display the plot using the show function.

Bottom line: This will create a simple 3×3 heatmap with a color bar, title, and axis labels.  

Customizations available in Matplotlib for heatmaps   

Following is a list of the customizations available for Heatmaps in Matplotlib:  

1. Changing the color map  
2. Changing the axis labels  
3. Changing the title  
4. Adding a color bar  
5. Adjusting the size and aspect ratio  
6. Setting the minimum and maximum values 
7. Adding annotations  
8. Adjusting the cell size 
9. Masking certain cells  
10. Adding borders  

 These are just a few examples of the many customizations that can be done in heatmaps using Matplotlib. Now, let’s see all the customizations being implemented in a single example code snippet:  

In this example, the heatmap is customized in the following ways:  

1. Set the colormap to ‘coolwarm’ 
2. Set the minimum and maximum values of the colormap using `vmin` and `vmax` 
3. Set the size of the figure using `figsize` 
4. Set the extent of the heatmap using `extent` 
5. Set the linewidth of the heatmap using `linewidth` 
6. Add a colorbar to the figure using the `colorbar` 
7. Set the title, xlabel, and ylabel using `set_title`, `set_xlabel`, and `set_ylabel`, respectively 
8. Add annotations to the heatmap using `text` 
9. Mask certain cells in the heatmap by setting their values to `np.nan` 
10. Show the frame around the heatmap using `set_frame_on(True)` 

Creating heatmaps using “Seaborn”  

We can create heatmaps using Seaborn by following the aforementioned steps:  

• First, we import the necessary libraries: seaborn, matplotlib, and numpy.
• Next, we generate a random 10×10 matrix of numbers using NumPy’s rand function and store it in the variable data.
• We create a heatmap by using Seaborn’s heatmap function. It takes the data as input and specifies the color map using the cmap parameter. Additionally, we set the annot parameter to True to display the values in each cell of the heatmap.
• To enhance the plot, we add a title, x-label, and y-label using Matplotlib’s title, xlabel, and ylabel functions.
• Finally, we display the plot using the show function from Matplotlib.

Overall, the code generates a random heatmap using Seaborn with a color map, annotations, and labels using Matplotlib.  

Customizations available in Seaborn for heatmaps:

Following is a list of the customizations available for Heatmaps in Seaborn:  

1. Change the color map  
2. Add annotations to the heatmap cells 
3. Adjust the size of the heatmap  
4. Display the actual numerical values of the data in each cell of the heatmap 
5. Add a color bar to the side of the heatmap 
6. Change the font size of the heatmap  
7. Adjust the spacing between cells  
8. Customize the x-axis and y-axis labels 
9. Rotate the x-axis and y-axis tick labels 

Now, let’s see all the customizations being implemented in a single example code snippet:

In this example, the heatmap is customized in the following ways:  

1. Set the color palette to “Blues”. 
2. Add annotations with a font size of 10. 
3. Set the x and y labels and adjust font size. 
4. Set the title of the heatmap. 
5. Adjust the figure size. 
6. Show the heatmap plot. 

Limitations of heatmaps:

Heatmaps are a useful visualization tool for exploring and analyzing data, but they do have some limitations that you should be aware of:  

• Limited to two-dimensional data: They are designed to visualize two-dimensional data, which means that they are not suitable for visualizing higher-dimensional data. 
• Limited to continuous data: They are best suited for continuous data, such as numerical values, as they rely on a color scale to convey the information. Categorical or binary data may not be as effectively visualized using heatmaps. 
• May be affected by color blindness: Some people are color blind, which means that they may have difficulty distinguishing between certain colors. This can make it difficult for them to interpret the information in a heatmap.
  

• Can be sensitive to scaling: The color mapping in a heatmap is sensitive to the scale of the data being visualized. Therefore, it is important to carefully choose the color scale and to consider normalizing or standardizing the data to ensure that the heatmap accurately represents the underlying data. 
• Can be misleading: They can be visually appealing and highlight patterns in the data, but they can also be misleading if not carefully designed. For example, choosing a poor color scale or omitting important data points can distort the visual representation of the data. 

It is important to consider these limitations when deciding whether or not to use a heatmap for visualizing your data.  

Conclusion

Heatmaps are powerful tools for visualizing data patterns and trends. They find applications in various fields, enabling easy interpretation and analysis of large datasets. Matplotlib and Seaborn offer flexible options to create and customize heatmaps. However, it’s essential to understand their limitations, such as two-dimensional data representation and sensitivity to color perception. By considering these factors, heatmaps can be a valuable asset in gaining insights and communicating information effectively.

Written by Safia Faiz

Unlock the full potential of your data with the power of data visualization! Go through this blog and discover why visualizations are crucial in Data Science and explore the most effective and game-changing types of visualizations that will revolutionize the way you interpret and extract insights from your data. Get ready to take your data analysis skills to the next level! 

What is data visualization?

Data visualization involves using different charts, graphs, and other visual elements to represent data and information graphically and the purpose of it is to make complex and hard to understand and complex datasets easily understandable, accessible, and interpretable.

This powerful tool enables businesses to explore, analyze and identify trends, patterns and relationships from the raw data that are usually hidden by just looking at the data itself or its statistics. 

By mastering the ability of data visualization, businesses and organizations can make effective and important decisions and actions based on the data and the insights gained. These decisions are additionally referred to as ‘Data-Driven Decisions’. By presenting data in a visual format, analysts can effectively communicate their findings to their team and to their clients, which is a challenging task as clients sometimes can’t interpret raw data and need a medium that they can interpret easily. 

Importance of data visualization

Here is a list of some benefits data visualization offers that make us understand its importance and its usefulness: 

1. Simplifying complex data: It enables complex data to be presented in a simplified and understandable manner. By using visual representations such as graphs and charts, data can be made more accessible to individuals who are not familiar with the underlying data. 

2. Enhancing insights: It can help to identify patterns and trends that might not be immediately apparent from raw data. By presenting data visually, it is easier to identify correlations and relationships between variables, enabling analysts to draw insights and make more informed decisions. 

3. Enhanced communication: It makes it easier to communicate complex data to a wider audience, including non-technical stakeholders in a way that is easy to understand and engage with. Visualizations can be used to tell a story, convey complex information, and facilitate collaboration among stakeholders, team members, and decision makers. 

4. Increasing efficiency: It can save time and increase efficiency by enabling analysts to quickly identify patterns and relationships in raw data. This can help to streamline the analysis process and enable analysts to focus their efforts on areas that are most likely to yield insights. 

5. Identifying anomalies and errors: It can help to identify errors or anomalies in the data. By presenting data visually, it is easier to spot outliers or unusual patterns that might indicate errors in data collection or processing. This can help analysts to clean and refine the data, ensuring that the insights derived from the data are accurate and reliable. 

6. Faster and more effective decision-making: It can help you make more informed and data-driven decisions by presenting information in a way that is easy to digest and interpret. Visualizations can help you identify key trends, outliers, and insights that can inform your decision-making, leading to faster and more effective outcomes. 

7. Improved data exploration and analysis: It enables you to explore and analyze your data in a more intuitive and interactive way. By visualizing data in different formats and at different levels of detail, you can gain new insights and identify areas for further exploration and analysis. 

Choosing the right type of visualization 

This is the only challenge faced when working with data visualizations, and to master this skill completely, you must have a clear idea about choosing the right type of visual for creating amazing, clear, attractive, and pleasing visuals. Keeping the following points in mind will help you in this: 

Identify purpose  

Before starting to create your visualization, it’s important to identify what your purpose is. Your purpose may include comparing different values and examining distributions, relationships, or compositions of variables. This step is important as each purpose has a different type of visualization that suits it best.

Understanding audience  

You can get help in choosing the best type of visualization for your message if you know about your audience, their preferences, and in which context they will view your visualization. This is useful as different visualizations are more effective with different audiences. 

Selecting the appropriate visual

Once you have identified your purpose and your audience, the final step is choosing the appropriate visualization to convey your message, some common visuals include: 

1. Comparison Charts: compare different groups/categories. 
2. Distribution Charts: show distributions of a variable. 
3. Relationship Charts: show the relationship between two or more variables. 
4. Composition Charts: show how a whole part is divided into its parts.

Ethics of data visualization & avoiding misleading representations 

In many cases, data visualization may also be used to misinterpret information intentionally or unintentionally. An example includes manipulating data by using specific scales or omitting specific data points to support a particular narrative and not showing the actual view of the data. Some considerations regarding the ethics of data visualization include: 

1. Accuracy of data: Data should be accurate and should not be presented in a way to misinterpret information. 
2. Appropriateness of visualization type: The type of visual selected should be appropriate for the data being presented and the message being conveyed. 
3. Clarity of message: The message conveyed through visualization should be clear and easy to understand. 
4. Avoiding bias and discrimination: Each data visualization should be clear of bias and discrimination. 

Avoiding misleading representations 

You want to represent your data in the most efficient way possible which can be easily interpreted and free of ambiguities, now that’s not always the case, there are times when your data can mislead your visualization and convey the wrong message. In those cases, you can take help from the following points to avoid misleadingness: 

• Use consistent scales and axes in your charts and graphs. 
• Avoid using truncated axes and skewed data ranges which cause data to appear less significant. 
• Label your data points and axes properly for clarity. 
• Avoid cherry-picking the data to support a particular narrative. 
• Provide clear and concise context for the data you are presenting. 

Types of data visualizations

There are numerous visualizations available, each with its own use and importance, and the choice of a visual depends on your need i.e., what kind of data you want to analyze, and what type of insight are you looking for. Nonetheless, here are some most common visuals used in data science:

• Bar Charts: Bar charts are normally used to compare categorical data, such as the frequency or proportion of different categories. They are used to visualize data that can be organized or split into different discrete groups or categories.
• Line Graphs: Line graphs are a type of visualization that uses lines to represent data values. They are typically used to represent continuous data.
• Scatter Plots: Scatter plot is a type of data visualization that displays the relationship between two quantitative (numerical) variables.  They are used to explore and analyze the correlation or association between two continuous variables.
• Histograms: A histogram graph represents the distribution of a continuous numerical variable by dividing it into intervals and counting the number of observations. They are used to visualize the shape and spread of data.

• Heatmaps: Heatmaps are commonly used to show the relationships between two variables, such as the correlation between different features in a dataset. 
• Box and Whisker Plots:  They are also known as boxplots and are used to display the distribution of a dataset. A box plot consists of a box that spans the first quartile (Q1) to the third quartile (Q3) of the data, with a line inside the box representing the median.
• Count Plots: A count plot is a type of bar chart that displays the number of occurrences of a categorical variable. The x-axis represents the categories, and the y-axis represents the count or frequency of each category.
• Point Plots: A point plot is a type of line graph that displays the mean (or median) of a continuous variable for each level of a categorical variable. They are useful for comparing the values of a continuous variable across different levels.
• Choropleth Maps: Choropleth map is a type of geographical visualization that uses color to represent data values for different geographic regions, such as countries, states, or counties.
• Tree Maps: This visualization is used to display hierarchical data as nested rectangles, with each rectangle representing a node in the hierarchy. Treemaps are useful for visualizing complex hierarchical data in a way that highlights the relative sizes and values of different nodes. 

Conclusion

So, this blog was all about introducing you to this powerful tool in the world of data science. Now you have a clear idea about what data visualization is, and what is its importance for analysts, businesses, and stakeholders.

You also learned about how you can choose the right type of visual, the ethics of data visualization and got familiar with 10 new different data visualizations and how they look like. The next step for you is to learn about how you can create these visuals using Python libraries such as matplotlib, seaborn and plotly. 

Researchers, statisticians, and data analysts rely on histograms to gain insights into data distributions, identify patterns, and detect outliers. Data scientists and machine learning practitioners use histograms as part of exploratory data analysis and feature engineering. Overall, anyone working with numerical data and seeking to gain a deeper understanding of data distributions can benefit from information on histograms.

Defining histograms

A histogram is a type of graphical representation of data that shows the distribution of numerical values. It consists of a set of vertical bars, where each bar represents a range of values, and the height of the bar indicates the frequency or count of data points falling within that range.   

Histograms are commonly used in statistics and data analysis to visualize the shape of a data set and to identify patterns, such as the presence of outliers or skewness. They are also useful for comparing the distribution of different data sets or for identifying trends over time. 

The picture above shows how 1000 random data points from a normal distribution with a mean of 0 and standard deviation of 1 are plotted in a histogram with 30 bins and black edges.   

 Advantages of histograms

• Visual Representation: Histograms provide a visual representation of the distribution of data, enabling us to observe patterns, trends, and anomalies that may not be apparent in raw data.
• Easy Interpretation: Histograms are easy to interpret, even for non-experts, as they utilize a simple bar chart format that displays the frequency or proportion of data points in each bin.
• Outlier Identification: Histograms are useful for identifying outliers or extreme values, as they appear as individual bars that significantly deviate from the rest of the bars.
• Comparison of Data Sets: Histograms facilitate the comparison of distribution between different data sets, enabling us to identify similarities or differences in their patterns.
• Data Summarization: Histograms are effective for summarizing large amounts of data by condensing the information into a few key features, such as the shape, center, and spread of the distribution.

 Creating a histogram using Matplotlib library

We can create histograms using Matplotlib by following a series of steps. Following the import statements of the libraries, the code generates a set of 1000 random data points from a normal distribution with a mean of 0 and standard deviation of 1, using the `numpy.random.normal()` function.  

1. The plt.hist() function in Python is a powerful tool for creating histograms. By providing the data, number of bins, bar color, and edge color as input, this function generates a histogram plot.
2. To enhance the visualization, the xlabel(), ylabel(), and title() functions are utilized to add labels to the x and y axes, as well as a title to the plot.
3. Finally, the show() function is employed to display the histogram on the screen, allowing for detailed analysis and interpretation.

Overall, this code generates a histogram plot of a set of random data points from a normal distribution, with 30 bins, blue bars, black edges, labeled axes, and a title. The histogram shows the frequency distribution of the data, with a bell-shaped curve indicating the normal distribution.     

Customizations available in Matplotlib for histograms    

In Matplotlib, there are several customizations available for histograms. These include:

1. Adjusting the number of bins. 
2. Changing the color of the bars. 
3. Changing the opacity of the bars. 
4. Changing the edge color of the bars. 
5. Adding a grid to the plot. 
6. Adding labels and a title to the plot. 
7. Adding a cumulative density function (CDF) line. 
8. Changing the range of the x-axis. 
9. Adding a rug plot. 

Now, let’s see all the customizations being implemented in a single example code snippet:  

In this example, the histogram is customized in the following ways:  

• The number of bins is set to `20` using the `bins` parameter. 
• The transparency of the bars is set to `0.5` using the `alpha` parameter. 
• The edge color of the bars is set to `black` using the `edgecolor` parameter. 
• The color of the bars is set to `green` using the `color` parameter. 
• The range of the x-axis is set to `(-3, 3)` using the `range` parameter. 
• The y-axis is normalized to show density using the `density` parameter. 
• Labels and a title are added to the plot using the `xlabel()`, `ylabel()`, and `title()` functions. 
• A grid is added to the plot using the `grid` function. 
• A cumulative density function (CDF) line is added to the plot using the `cumulative` parameter and `histtype=’step’`. 
• A rug plot showing individual data points is added to the plot using the `plot` function. 

Creating a histogram using ‘Seaborn’ library: 

We can create histograms using Seaborn by following the steps:  

• First and foremost, importing the libraries: `NumPy`, `Seaborn`, `Matplotlib`, and `Pandas`. After importing the libraries, a toy dataset is created using `pd.DataFrame()` of 1000 samples that are drawn from a normal distribution with mean 0 and standard deviation 1 using NumPy’s `random.normal()` function.  
• We use Seaborn’s `histplot()` function to plot a histogram of the ‘data’ column of the DataFrame with `20` bins and a `blue` color.  
• The plot is customized by adding labels, and a title, and changing the style to a white grid using the `set_style()` function.  
• Finally, we display the plot using the `show()` function from matplotlib.  

   

Overall, this code snippet demonstrates how to use Seaborn to plot a histogram of a dataset and customize the appearance of the plot quickly and easily.  

 Customizations available in Seaborn for histograms

Following is a list of the customizations available for Histograms in Seaborn:  

1. Change the number of bins. 
2. Change the color of the bars. 
3. Change the color of the edges of the bars. 
4. Overlay a density plot on the histogram. 
5. Change the bandwidth of the density plot. 
6. Change the type of histogram to cumulative. 
7. Change the orientation of the histogram to horizontal. 
8. Change the scale of the y-axis to logarithmic. 

Now, let’s see all these customizations being implemented here as well, in a single example code snippet:  

 In this example, we have done the following customizations: 

1. Set the number of bins to `20`. 
2. Set the color of the bars to `green`. 
3. Set the `edgecolor` of the bars to `black`. 
4. Added a density plot overlaid on top of the histogram using the `kde` parameter set to `True`. 
5. Set the bandwidth of the density plot to `0.5` using the `kde_kws` parameter. 
6. Set the histogram to be cumulative using the `cumulative` parameter. 
7. Set the y-axis scale to logarithmic using the `log_scale` parameter. 
8. Set the title of the plot to ‘Customized Histogram’. 
9. Set the x-axis label to ‘Values’. 
10. Set the y-axis label to ‘Frequency’. 

Limitations of Histograms: 

 Histograms are widely used for visualizing the distribution of data, but they also have limitations that should be considered when interpreting them. These limitations are jotted down below:  

1. They can be sensitive to the choice of bin size or the number of bins, which can affect the interpretation of the distribution. Choosing too few bins can result in a loss of information while choosing too many bins can create artificial patterns and noise. 
2. They can be influenced by outliers, which can skew the distribution or make it difficult to see patterns in the data. 
3. They are typically univariate and cannot capture relationships between multiple variables or dimensions of data. 
4. Histograms assume that the data is continuous and does not work well with categorical data or data with large gaps between values. 
5. They can be affected by the choice of starting and ending points, which can affect the interpretation of the distribution. 
6. They do not provide information on the shape of the distribution beyond the binning intervals. 

 It’s important to consider these limitations when using histograms and to use them in conjunction with other visualization techniques to gain a more complete understanding of the data.  

 Wrapping up

In conclusion, histograms are powerful tools for visualizing the distribution of data. They provide valuable insights into the shape, patterns, and outliers present in a dataset. With their simplicity and effectiveness, histograms offer a convenient way to summarize and interpret large amounts of data.

By customizing various aspects such as the number of bins, colors, and labels, you can tailor the histogram to your specific needs and effectively communicate your findings. So, embrace the power of histograms and unlock a deeper understanding of your data.

Written by Safia Faiz

Data visualization is the art of presenting complex information in a way that is easy to understand and analyze. With the explosion of data in today’s business world, the ability to create compelling data visualizations has become a critical skill for anyone working with data.

Whether you’re a business analyst, data scientist, or marketer, the ability to communicate insights effectively is key to driving business decisions and achieving success. 

In this article, we’ll explore the art of data visualization and how it can be used to tell compelling stories with business analytics. We’ll cover the key principles of data visualization and provide tips and best practices for creating stunning visualizations. So, grab your favorite data visualization tool, and let’s get started! 

Importance of data visualization in business analytics  

Data visualization is the process of presenting data in a graphical or pictorial format. It allows businesses to quickly and easily understand large amounts of complex information, identify patterns, and make data-driven decisions. Good data visualization can spot the difference between an insightful analysis and a meaningless spreadsheet. It enables stakeholders to see the big picture and identify key insights that may have been missed in a traditional report. 

Benefits of data visualization 

Data visualization has several advantages for business analytics, including 

1. Improved communication and understanding of data 

Visualizations make it easier to communicate complex data to stakeholders who may not have a background in data analysis. By presenting data in a visual format, it is easier to understand and interpret, allowing stakeholders to make informed decisions based on data-driven insights. 

2. More effective decision making 

Data visualization enables decision-makers to identify patterns, trends, and outliers in data sets, leading to more effective decision-making. By visualizing data, decision-makers can quickly identify correlations and relationships between variables, leading to better insights and more informed decisions. 

3. Enhanced ability to identify patterns and trends 

Visualizations enable businesses to identify patterns and trends in their data that may be difficult to detect using traditional data analysis methods. By identifying these patterns, businesses can gain valuable insights into customer behavior, product performance, and market trends. 

4. Increased engagement with data 

Visualizations make data more engaging and interactive, leading to increased interest and engagement with data. By making data more accessible and interactive, businesses can encourage stakeholders to explore data more deeply, leading to a deeper understanding of the insights and trends 

5. Principles of effective data visualization 

Effective data visualization is more than just putting data into a chart or graph. It requires careful consideration of the audience, the data, and the message you are trying to convey. Here are some principles to keep in mind when creating effective data visualizations: 

6. Know your audience

Understanding your audience is critical to creating effective data visualizations. Who will be viewing your visualization? What are their backgrounds and areas of expertise? What questions are they trying to answer? Knowing your audience will help you choose the right visualization format and design a visualization that is both informative and engaging. 

7. Keep it simple 

Simplicity is key when it comes to data visualization. Avoid cluttered or overly complex visualizations that can confuse or overwhelm your audience. Stick to key metrics or data points, and choose a visualization format that highlights the most important information. 

8. Use the right visualization format 

Choosing the right visualization format is crucial to effectively communicate your message. There are many different types of visualizations, from simple bar charts and line graphs to more complex heat maps and scatter plots. Choose a format that best suits the data you are trying to visualize and the story you are trying to tell. 

9. Emphasize key findings 

Make sure your visualization emphasizes the key findings or insights that you want to communicate. Use color, size, or other visual cues to draw attention to the most important information. 

10. Be consistent 

Consistency is important when creating data visualizations. Use a consistent color palette, font, and style throughout your visualization to make it more visually appealing and easier to understand. 

Tools and techniques for data visualization 

There are many tools and techniques available to create effective data visualizations. Some of them are:

1. Excel 

Microsoft Excel is one of the most commonly used tools for data visualization. It offers a wide range of chart types and customization options, making it easy to create basic visualizations.

2. Tableau 

Tableau is a powerful data visualization tool that allows users to connect to a wide range of data sources and create interactive dashboards and visualizations. Tableau is easy to use and provides a range of visualization options that are customizable to suit different needs. 

3. Power BI 

Microsoft Power BI is another popular data visualization tool that allows you to connect to various data sources and create interactive visualizations, reports, and dashboards. It offers a range of customizable visualization options and is easy to use for beginners.  

4. D3.js 

D3.js is a JavaScript library used for creating interactive and customizable data visualizations on the web. It offers a wide range of customization options and allows for complex visualizations. 

5. Python Libraries 

Python libraries such as Matplotlib, Seaborn, and Plotly can be used for data visualization. These libraries offer a range of customizable visualization options and are widely used in data science and analytics. 

6. Infographics 

Infographics are a popular tool for visual storytelling and data visualization. They combine text, images, and data visualizations to communicate complex information in a visually appealing and easy-to-understand way. 

7. Looker Studio 

Looker Studio is a free data visualization tool that allows users to create interactive reports and dashboards using a range of data sources. Looker Studio is known for its ease of use and its integration with other Google products. 

Data Visualization in action: Examples from business analytics 

To illustrate the power of data visualization in business analytics, let’s take a look at a few examples: 

1. Sales Performance Dashboard

A sales performance dashboard is a visual representation of sales data that provides insight into sales trends, customer behavior, and product performance. The dashboard may include charts and graphs that show sales by region, product, and customer segment. By analyzing this data, businesses can identify opportunities for growth and optimize their sales strategy. 

2. Website analytics dashboard

A website analytics dashboard is a visual representation of website performance data that provides insight into visitor behavior, content engagement, and conversion rates. The dashboard may include charts and graphs that show website traffic, bounce rates, and conversion rates. By analyzing this data, businesses can optimize their website design and content to improve user experience and drive conversions. 

3. Social media analytics dashboard

A social media analytics dashboard is a visual representation of social media performance data that provides insight into engagement, reach, and sentiment. The dashboard may include charts and graphs that show engagement rates, follower growth, and sentiment analysis. By analyzing this data, businesses can optimize their social media strategy and improve engagement with their audience. 

Frequently Asked Questions (FAQs) 

Q: What is data visualization? 

A: Data visualization is the process of transforming complex data into visual representations that are easy to understand. 

Q: Why is data visualization important in business analytics?

A: Data visualization is important in business analytics because it enables businesses to communicate insights, trends, and patterns to key stakeholders in a way that is both clear and engaging. 

Q: What are some common mistakes in data visualization? 

A: Common mistakes in data visualization include overloading with data, using inappropriate visualizations, ignoring the audience, and being too complicated. 

Conclusion 

In conclusion, the art of data visualization is an essential skill for any business analyst who wants to tell compelling stories via data. Through effective data visualization, you can communicate complex information in a clear and concise way, allowing stakeholders to understand and act upon the insights provided. By using the right tools and techniques, you can transform your data into a compelling narrative that engages your audience and drives business growth. 

Written by Yogini Kuyate

Are you interested in learning Python for Data Science? Look no further than Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use the power of Python to perform data analysis, visualization, and manipulation. 

Python is a powerful programming language used in data science, machine learning, and artificial intelligence. It is a versatile language that is easy to learn and has a wide range of applications. In this course, you will learn the basics of Python programming and how to use it for data analysis and visualization.

Learn the basics of Python programming and how to use it for data analysis and visualization in Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use Python to perform data analysis, visualization, and manipulation. 

Why learn Python for data science? 

Python is a popular language for data science because it is easy to learn and use. It has a large community of developers who contribute to open-source libraries that make data analysis and visualization more accessible. Python is also an interpreted language, which means that you can write and run code without the need for a compiler. 

Python has a wide range of applications in data science, including: 

• Data analysis: Python is used to analyze data from various sources such as databases, CSV files, and APIs. 
• Data visualization: Python has several libraries that can be used to create interactive and informative visualizations of data. 
• Machine learning: Python has several libraries for machine learning, such as scikit-learn and TensorFlow. 
• Web scraping: Python is used to extract data from websites and APIs.
  

Python for Data Science Course Outline 

Data Science Dojo’s Introduction to Python for Data Science course covers the following topics: 

• Introduction to Python: Learn the basics of Python programming, including data types, control structures, and functions. 
• NumPy: Learn how to use the NumPy library for numerical computing in Python. 
• Pandas: Learn how to use the Pandas library for data manipulation and analysis. 
• Data visualization: Learn how to use the Matplotlib and Seaborn libraries for data visualization. 
• Machine learning: Learn the basics of machine learning in Python using sci-kit-learn. 
• Web scraping: Learn how to extract data from websites using Python. 
• Project: Apply your knowledge to a real-world Python project.

Python is an important programming language in the data science field and learning it can have significant benefits for data scientists. Here are some key points and reasons to learn Python for data science, specifically from Data Science Dojo’s instructor-led live training program: 

• Python is easy to learn: Compared to other programming languages, Python has a simpler and more intuitive syntax, making it easier to learn and use for beginners. 
• Python is widely used: Python has become the preferred language for data science and is used extensively in the industry by companies such as Google, Facebook, and Amazon. 
• Large community: The Python community is large and active, making it easy to get help and support. 
• A comprehensive set of libraries: Python has a comprehensive set of libraries specifically designed for data science, such as NumPy, Pandas, Matplotlib, and Scikit-learn, making data analysis easier and more efficient. 
• Versatile: Python is a versatile language that can be used for a wide range of tasks, from data cleaning and analysis to machine learning and deep learning. 
• Job opportunities: As more and more companies adopt Python for data science, there is a growing demand for professionals with Python skills, leading to more job opportunities in the field. 

Data Science Dojo’s instructor-led live training program provides a structured and hands-on learning experience to master Python for data science. The program covers the fundamentals of Python programming, data cleaning and analysis, machine learning, and deep learning, equipping learners with the necessary skills to solve real-world data science problems.  

By enrolling in the program, learners can benefit from personalized instruction, hands-on practice, and collaboration with peers, making the learning process more effective and efficient.

Some common questions asked about the course 

• What are the prerequisites for the course? 

The course is designed for individuals with little to no programming experience. However, some familiarity with programming concepts such as variables, functions, and control structures is helpful. 

• What is the format of the course? 

The course is an instructor-led live training course. You will attend live online classes with a qualified instructor who will guide you through the course material and answer any questions you may have. 

• How long is the course? 

The course is four days long, with each day consisting of six hours of instruction. 

Explore the Power of Python for Data Science

If you’re interested in learning Python for Data Science, Data Science Dojo’s Introduction to Python for Data Science course is an excellent place to start. This course will provide you with a solid foundation in Python programming and teach you how to use Python for data analysis, visualization, and manipulation.  

With its instructor-led live training format, you’ll have the opportunity to learn from an experienced instructor and interact with other students.

Enroll today and start your journey to becoming a data scientist with Python.

Data is an essential component of any business, and it is the role of a data analyst to make sense of it all. Power BI is a powerful data visualization tool that helps them turn raw data into meaningful insights and actionable decisions.

In this blog, we will explore the role of data analysts and how they use Power BI to extract insights from data and drive business success. From data discovery and cleaning to report creation and sharing, we will delve into the key steps that can be taken to turn data into decisions. 

A data analyst is a professional who uses data to inform business decisions. They process and analyze large sets of data to identify trends, patterns, and insights that can help organizations make more informed decisions. 

Who is a data analyst?

A data analyst is a professional who works with data to extract insights, draw conclusions, and support decision-making. They use a variety of tools and techniques to clean, transform, visualize, and analyze data to understand patterns, relationships, and trends. The role of a data analyst is to turn raw data into actionable information that can inform and drive business strategy.

They use various tools and techniques to extract insights from data, such as statistical analysis, and data visualization. They may also work with databases and programming languages such as SQL and Python to manipulate and extract data. 

The importance of data analysts in an organization is that they help organizations make data-driven decisions. By analyzing data, analysts can identify new opportunities, optimize processes, and improve overall performance. They also help organizations make more informed decisions by providing insights into customer behavior, market trends, and other key metrics.

Additionally, their role and job can help organizations stay competitive by identifying areas where they may be lagging and providing recommendations for improvement. 

Defining Power BI 

Power BI provides a suite of data visualization and analysis tools to help organizations turn data into actionable insights. It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. 

Check out this course and learn Power BI today!

The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards. It enables organizations to quickly create and share visualizations, reports, and dashboards with stakeholders, regardless of their technical skill level.

Power BI also provides collaboration features, allowing team members to work together on data insights, and share information and insights with others through Power BI reports and dashboards. 

Key capabilities of Power BI  

Data Connectivity:It allows users to connect to various data sources including Excel, SQL Server, Azure SQL, and other cloud-based data sources. 

Data Transformation: It provides a wide range of data transformation tools that allow users to clean, shape, and prepare data for analysis. 

Visualization: It offers a wide range of visualization options, including charts, tables, and maps, that allow users to create interactive and visually appealing reports. 

Sharing and Collaboration: It allows users to share and collaborate on reports and visualizations with others in their organization. 

Mobile Access: It also offers mobile apps for iOS and Android, that allow users to access and interact with their data on the go. 

How does a data analyst use Power BI? 

A data analyst uses Power BI to collect, clean, transform, visualize, and analyze data to turn it into meaningful insights and decisions. The following steps outline the process of using Power BI for data analysis: 

1. Connect to data sources: A data analyst can import data from a variety of sources, such as spreadsheets, databases, or cloud-based services. Power BI provides several ways to import data, including manual upload, data connections, and direct connections to data sources. 
2. Clean and transform data: Before data can be analyzed, it often needs to be cleaned and prepared. This may include removing any extraneous information, correcting errors or inconsistencies, and transforming data into a format that is usable for analysis.
3. Create visualizations: Once the data has been prepared, a data analyst can use Power BI to create visualizations of the data. This may include bar charts, line graphs, pie charts, scatter plots, and more. Power BI provides a few built-in visualizations and the ability to create custom visualizations, giving data analysts a wide range of options for presenting data. 
4. Perform data analysis: Power BI provides a range of data analysis tools, including calculated fields and measures, and the DAX language, which allows data analysts to perform more advanced analysis. These tools allow them to uncover insights and trends that might not be immediately apparent. 
5. Collaborate and share insights: Once insights have been uncovered, data analysts can share their findings with others through Power BI reports or dashboards. These reports provide a way to present data visualizations and analysis results to stakeholders and can be published and shared with others. 

 

Learn Power BI with this crash course in no time!

By following these steps, a data analyst can use Power BI to turn raw data into meaningful insights and decisions that can inform business strategy and decision-making. 

Why should you use data analytics with Power BI? 

User-friendly interface – Power BI has a user-friendly interface, which makes it easy for users with little to no technical skills to create and share interactive dashboards, reports, and visualizations. 

Real-time data visualization – It provides real-time data visualization, allowing users to analyze data in real time and make quick decisions. 

Integration with other Microsoft tools – Power BI integrates seamlessly with other Microsoft tools, such as Excel, SharePoint, and Azure, making it an ideal tool for organizations using Microsoft technology. 

Wide range of data sources – It can connect to a wide range of data sources, including databases, spreadsheets, cloud services, and web APIs, making it easy to consolidate data from multiple sources. 

Cost-effective – It is a cost-effective solution for data analytics, with both free and paid versions available, making it accessible to organizations of all sizes. 

Mobile accessibility – Power BI provides mobile accessibility, allowing users to access and analyze data from anywhere, on any device. 

Collaboration features – With robust collaboration features, it allows users to share dashboards and reports with other team members, encouraging teamwork and decision-making. 

Conclusion 

In conclusion, Power BI is a powerful tool for data analysis that provides organizations with the ability to easily visualize, analyze, and share complex data. By preparing, cleaning, and transforming data, creating relationships between tables, using visualizations and DAX, they can create reports and dashboards that provide valuable insights into key business metrics.

The ability to publish reports, share insights, and collaborate with others makes Power BI an essential tool for any organization looking to improve performance and make informed decisions.

Big data is conventionally understood in terms of its scale. This one-dimensional approach, however, runs the risk of simplifying the complexity of big data. In this blog, we discuss the 10 Vs as metrics to gauge the complexity of big data. 

When we think of “big data,” it is easy to imagine a vast, intangible collection of customer information and relevant data required to grow your business. But the term “big data” isn’t about size – it’s also about the potential to uncover valuable insights by considering a range of other characteristics. In other words, it’s not just about the amount of data we have, but also how we use and analyze it. 

Volume 

The most obvious feature is the volume that captures the sheer scale of a certain dataset. Consider, for example, 40,000 apps added to the app store each year. Similarly, 1 in 40,000 searches are made over Google every second. 

Big numbers carry the immediate appeal of big data. Whether it is the 2.2 billion active monthly users on Facebook or the 2.2 billion cups of coffee that are consumed in single day, big numbers capture qualities about large swathes of population, conveying insights that can feel universal in their scale.  

As another example, consider the 294 billion emails being sent every day. In comparison, there are 300 billion stars in the Milky Way. Somehow, the largeness of these numbers in a human context can help us make better sense of otherwise unimaginable quantities like the stars in the Milky Way! 

Velocity 

In nearly all the examples considered above, velocity of the data was also an important feature. Velocity adds to volume, allowing us to grapple with data as a dynamic quantity. In big data it refers to how quickly data is generated and how fast it moves. It is one of the three Vs of big data, along with volume and variety. Velocity is important for businesses that need their data to be quickly available for making informed decisions. 

Variety 

Variety, here, refers to the several types of data that are constantly in circulation and is an integral quality of big data. Different data sets are unstructured. This includes data shared over social media and instant messaging regularly such as videos, audio, and phone recordings. 

Then, there is the 10% semi-structured data in circulation including emails, webpages, zipped files, etc. Lastly, there is the rarity of structured data such as financial transactions. 

Data types are a defining feature of big data as unstructured data needs to be cleaned and structured before it can be used for data analytics. In fact, the availability of clean data is among the top challenges facing data scientists. According to Forbes, most data scientists spend 60% of their time cleaning data.  

Variability 

Variability is a measure of the inconsistencies in data and is often confused with variety. To understand variability, let us consider an example. You go to a coffee shop every day and purchase the same latte each day. However, it may smell or taste slightly or significantly different each day.  

This kind of inconsistency in data is an important feature as it places limits on the reproducibility of data. This is particularly relevant in sentiment analysis which is much harder for AI models as compared to humans. Sentiment analysis requires an additional level of input, i.e., context.  

An example of variability in big data can be seen when investigating the amount of time spent on phones daily by diverse groups of people. The data collected from different samples (high school students, college students, and adult full-time employees) can vary, resulting in variability. Another example could be a soda shop offering different blends of soda but having different taste every day, which is variability. 

Variability also accounts for the inconsistent speed at which data is downloaded and stored across various systems, creating a unique experience for customers consuming the same data.  

Veracity 

Veracity refers to the reliability of the data source. Numerous factors can contribute to the reliability of the input they provide at a particular time in a particular situation. 

Veracity is particularly important for making data-driven decisions for businesses as reproducibility of patterns relies heavily on the credibility of initial data inputs. 

Validity 

Validity pertains to the accuracy of data for its intended use. For example, you may acquire a dataset pertaining to data related to your subject of inquiry, increasing the task of forming a meaningful relationship and inquiry. Registered charity data contact lists 

Volatility

Volatility refers to the time considerations placed on a particular data set. It involves considering if data acquired a year ago would be relevant for analysis for predictive modeling today. This is specific to the analyses being performed. Similarly, volatility also means gauging whether a particular data set is historic or not. Usually, data volatility comes under data governance and is assessed by data engineers.  

Vulnerability 

Big data is often about consumers. We often overlook the potential harm in sharing our shopping data, but the reality is that it can be used to uncover confidential information about an individual. For instance, Target accurately predicted a teenage girl’s pregnancy before her own parents knew it. To avoid such consequences, it’s important to be mindful of the information we share online. 

Visualization  

With a new data visualization tool being released every month or so, visualizing data is key to insightful results. The traditional x-y plot no longer suffices for the kind of complex detailing that goes into categorizations and patterns across various parameters obtained via big data analytics.  

Value 

BIG data is nothing if it cannot produce meaningful value. Consider, again, the example of Target using a 16-year-old’s shopping habits to predict her pregnancy. While in this case, it violates privacy, in most other cases, it can generate incredible customer value by bombarding them with the specific product advertisement they require. 

Learn about 10 Vs of big data by George Firican

10 Vs of Big Data 

Enable smart decision making with big data visualization

The 10 Vs of big data are Volume, Velocity, Variety, Veracity, Variability, Value, Viscosity, Volume growth rate, Volume change rate, and Variance in volume change rate. These are the characteristics of big data and help to understand its complexity.

The skills needed to work with big data involve coding, although the level of knowledge required for coding is not as deep as that of a programmer. Big Data and Data Science are two concepts that play a crucial role in enabling data-driven decision making. 90% of the world’s data has been created in the last two years, providing an incredible amount of data being created daily.

Companies employ data scientists to use data mining and big data to learn more about consumers and their behaviors. Both Data Mining and Big Data Analysis are major elements of data science. 

Small Data, on the other hand, is collected in a more controlled manner,  whereas Big Data refers to data sets that are too large or complex to be processed by traditional data processing applications. 

In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. We will also be sharing code snippets so you can try out different analysis techniques yourself. So, without any further ado let’s dive right in. 

What is Exploratory Data Analysis (EDA)? 

“The greatest value of a picture is when it forces us to notice what we never expected to see.”  John Tukey, American Mathematician 

A core skill to possess for someone who aims to pursue data science, data analysis or affiliated fields as a career is exploratory data analysis (EDA). To put it simply, the goal of EDA is to discover underlying patterns, structures, and trends in the datasets and drive meaningful insights from them that would help in driving important business decisions. 

The data analysis process enables analysts to gain insights into the data that can inform further analysis, modeling, and hypothesis testing.  

EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization. These activities together help in generating hypotheses, identifying potential data cleaning issues, and informing the choice of models or modeling techniques for further analysis. The results of EDA can be used to improve the quality of the data, to gain a deeper understanding of the data, and to make informed decisions about which techniques or models to use for the next steps in the data analysis process. 

Often it is assumed that EDA is to be performed only at the start of the data analysis process, however the reality is in contrast to this popular misconception, as stated EDA is an iterative process and can be revisited numerous times throughout the analysis life cycle if need may arise.  

In this blog while highlighting the importance and different renowned techniques of EDA we will also show you examples with code so you can try them out yourselves and better comprehend what this interesting skill is all about. 

Note: the dataset used for this purpose can be found at: https://www.kaggle.com/datasets/raniahelmy/no-show-investigate-dataset  

Want to see some exciting visuals that we can create from this dataset? DSD got you covered! Visit the link  

Importance of EDA: 

One of the key advantages of EDA is that it allows you to develop a deeper understanding of your data before you begin modelling or building more formal, inferential models. This can help you identify  

• Important variables,  
• Understand the relationships between variables, and  
• Identify potential issues with the data, such as missing values, outliers, or other problems that might affect the accuracy of your models. 

Another advantage of EDA is that it helps in generating new insights which may incur associated hypotheses, those hypotheses then can be tested and explored to gain a better understanding of the dataset. 

Finally, EDA helps you uncover hidden patterns in a dataset that were not comprehensible to the naked eye, these patterns often lead to interesting factors that one couldn’t even think would affect the target variable. 

Want to start your EDA journey, well you can always get yourself registered at Data Science Bootcamp.  

Common EDA techniques: 

The technique you employ for EDA is intertwined with the task at hand, many times you would not require implementing all the techniques, on the other hand there would be times that you’ll need accumulation of the techniques to gain valuable insights. To familiarize you with a few we have listed some of the popular techniques that would help you in EDA. 

Visualization:  

One of the most popular and effective ways to explore data is through visualization. Some popular types of visualizations include histograms, pie charts, scatter plots, box plots and much more. These can help you understand the distribution of your data, identify patterns, and detect outliers. 

Below are a few examples on how you can use visualization aspect of EDA to your advantage: 

Histogram: 

The histogram is a kind of visualization that shows the frequencies of each category in a dataset. 

The above graph shows us the number of responses belonging to different age groups and they have been partitioned based on how many came to the appointment and how many did not show up. 

Pie Chart: 

A pie chart is a circular image, it is usually used for a single feature to indicate how the data of that feature are distributed, commonly represented in percentages. 

The pie chart shows the distribution that 20.2% of the total data comprises of individuals who did not show up for the appointment while 79.8% of individuals did show up. 

Box Plot: 

Box plot is also an important kind of visualization that is used to check how the data is distributed, it shows the five number summary of the dataset, which is quite useful in many aspects such as checking if the data is skewed, or detecting the outliers etc.  

The box plot shows the distribution of the Age column, segregated on the basis of individuals who showed and did not show up for the appointments. 

Descriptive statistics:  

Descriptive statistics are a set of tools for summarizing data in a way that is easy to understand. Some common descriptive statistics include mean, median, mode, standard deviation, and quartiles. These can provide a quick overview of the data and can help identify the central tendency and spread of the data.

Grouping and aggregating:  

One way to explore a dataset is by grouping the data by one or more variables, and then aggregating the data by calculating summary statistics. This can be useful for identifying patterns and trends in the data. 

Data cleaning:  

Exploratory data analysis also includes cleaning data, it may be necessary to handle missing values, outliers, or other data issues before proceeding with further analysis.  

As you can see, fortunately this dataset did not have any missing value. 

Correlation analysis: 

Correlation analysis is a technique for understanding the relationship between two or more variables. You can use correlation analysis to determine the degree of association between variables, and whether the relationship is positive or negative. 

The heatmap indicates to what extent different features are correlated to each other, with 1 being highly correlated and 0 being no correlation at all. 

Types of EDA: 

There are a few different types of exploratory data analysis (EDA) that are commonly used, depending on the nature of the data and the goals of the analysis. Here are a few examples: 

Univariate EDA:  

Univariate EDA, short for univariate exploratory data analysis, examines the properties of a single variable by techniques such as histograms, statistics of central tendency and dispersion, and outliers detection. This approach helps understand the basic features of the variable and uncover patterns or trends in the data. 

The pie chart indicates what percentage of individuals from the total data are identified as alcoholic. 

Bivariate EDA:  

This type of EDA is used to analyse the relationship between two variables. It includes techniques such as creating scatter plots and calculating correlation coefficients and can help you understand how two variables are related to each other.

The bar chart shows what percentage of individuals are alcoholic or not and whether they showed up for the appointment or not. 

Multivariate EDA:  

This type of EDA is used to analyze the relationships between three or more variables. It can include techniques such as creating multivariate plots, running factor analysis, or using dimensionality reduction techniques such as PCA to identify patterns and structure in the data.

The above visualization is distplot of kind, bar, it shows what percentage of individuals belong to one of the possible four combinations diabetes and hypertension, moreover they are segregated on the basis of gender and whether they showed up for appointment or not.  

Time-series EDA:  

This type of EDA is used to understand patterns and trends in data that are collected over time, such as stock prices or weather patterns. It may include techniques such as line plots, decomposition, and forecasting. 

This kind of chart helps us gain insight of the time when most appointments were scheduled to happen, as you can see around 80k appointments were made for the month of May.

Spatial EDA:  

This type of EDA deals with data that have a geographic component, such as data from GPS or satellite imagery. It can include techniques such as creating choropleth maps, density maps, and heat maps to visualize patterns and relationships in the data.

In the above map, the size of the bubble indicates the number of appointments booked in a particular neighborhood while the hue indicates the percentage of individuals who did not show up for the appointment.  

Popular libraries for EDA: 

Following is a list of popular libraries that python has to offer which you can use for Exploratory Data Analysis.   

1. Pandas: This library offers efficient, adaptable, and clear data structures meant to simplify handling “relational” or “labelled” data. It is a useful tool for manipulating and organizing data. 
2. NumPy: This library provides functionality for handling large, multi-dimensional arrays and matrices of numerical data. It also offers a comprehensive set of high-level mathematical operations that can be applied to these arrays. It is a dependency for various other libraries, including Pandas, and is considered a foundational package for scientific computing using Python. 
3. Matplotlib: Matplotlib is a Python library used for creating plots and visualizations, utilizing NumPy. It offers an object-oriented interface for integrating plots into applications using various GUI toolkits such as Tkinter, wxPython, Qt, and GTK. It has a diverse range of options for creating static, animated, and interactive plots. 
4. Seaborn: This library is built on top of Matplotlib and provides a high-level interface for drawing statistical graphics. It’s designed to make it easy to create beautiful and informative visualizations, with a focus on making it easy to understand complex datasets. 
5. Plotly: This library is a data visualization tool that creates interactive, web-based plots. It works well with the pandas library and it’s easy to create interactive plots with zoom, hover, and other features. 
6. Altair: is a declarative statistical visualization library for Python. It allows you to quickly and easily create statistical graphics in a simple, human-readable format. 

Conclusion: 

In conclusion, Exploratory Data Analysis (EDA) is a crucial skill for data scientists and analysts, which includes data cleaning, manipulation, and visualization to discover underlying patterns and trends in the data. It helps in generating new insights, identifying potential issues and informing the choice of models or techniques for further analysis.

It is an iterative process that can be revisited throughout the data analysis life cycle. Overall, EDA is an important skill that can inform important business decisions and generate valuable insights from data. 

Data visualization is key to effective communication across all organizations. In this blog, we briefly introduce 33 tools to visualize data. 

Data-driven enterprises are evidently the new normal. Not only does this require companies to wrestle with data for internal and external decision-making challenges, but also requires effective communication. This is where data visualization comes in. 

Without visualization results found via rigorous data analytics procedures, key analyses could be forgone. Here’s where data visualization methods such as charts, graphs, scatter plots, 3D visualization, and so on, simplify the task. Visual data is far easier to absorb, retain, and recall.  

And so, we describe a total of 33 data visualization tools that offer a plethora of possibilities.  

Recommended data visualization tools you must know about  

Using these along with data visualization tips ensures healthy communication of results across organizations. 

1. Visual.ly 

Popular for its incredible distribution network which allows data import and export to third parties, Visual.ly is a great data visualization tool in the market.  

2. Sisense 

Known for its agility, Sisense provides immediate data analytics by means of effective data visualization. This tool identifies key patterns and summarizes data statistics, assisting data-driven strategies. 

3. Data wrapper 

Data Wrapper, a popular and free data visualization tool, produces quick charts and other graphical presentations of the statistics of big data.  

4. Zoho reports 

Zoho Reports is a straightforward data visualization tool that provides online reporting services on business intelligence. 

5. Highcharts 

The Highcharts visualization tool is used by many global top companies and works seamlessly in visualizing big data analytics.  

6. Qlikview 

Providing solutions to around 40,000 clients across a hundred countries, Qlickview’s data visualization tools provide features such as customized visualization and enterprise reporting for business intelligence. 

7. Sigma.js 

A JavaScript library for creating graphs, Sigma uplifts developers by making it easier to publish networks on websites.  

8. JupyteR 

A strongly rated, web-based application, JupyteR allows users to share and create documents with equations, code, text, and other visualizations.  

9. Google charts 

Another major data visualization tool, Google charts is popular for its ability to create graphical and pictorial data visualizations. 

10. Fusioncharts 

Fusioncharts is a Javascript-based data visualization tool that provides up to ninety chart-building packages that seamlessly integrate with significant platforms and frameworks.  

11. Infogram 

Infogram is a popular web-based tool used for creating infographics and visualizing data.  

12. Polymaps 

A free Javascript-based library, Polymaps allows users to create interactive maps in web browsers such as real-time display of datasets. 

13. Tableau 

Tableau allows its users to connect with various data sources, enabling them to create data visualization by means of maps, dashboards, stories, and charts, via a simple drag-and-drop interface. Its applications are far-reaching such as exploring healthcare data.  

14. Klipfolio 

Klipfolio provides immediate data from hundreds of services by means of pre-built instant metrics. It’s ideal for businesses that require custom dashboards 

15. Domo 

Domo is especially great for small businesses thanks to its accessible interface allowing users to create advanced charts, custom apps, and other data visualizations that assist them in making data-driven decisions.  

16. Looker 

A versatile data visualization tool, Looker provides a directory of various visualization types from bar gauges to calendar heat maps.  

17. Qlik sense 

Qlik Sense uses artificial intelligence to make data more understandable and usable. It provides greater interactivity, quick calculations, and the option to integrate data from hundreds of sources. 

18. Grafana 

Allowing users to create dynamic dashboards and offering other visualizations, Grafana is a great open-source visualization software.  

19. Chartist.js 

This free, open-source Javascript library allows users to create basic responsive charts that offer both customizability and compatibility across multiple browsers.  

20. Chart.js 

A versatile Javascript library, Chart.js is open source and provides a variety of 8 chart types while allowing animation and interaction.  

21. D3.js 

Another Javascript library, D3.js requires some Javascript knowledge and is used to manipulate documents via data.  

22. ChartBlocks 

ChartBlocks allows data import from nearly any source. It further provides detailed customization of visualizations created. 

23. Microsoft Power BI 

Used by nearly 200K+ organizations, Microsoft Power BI is a data visualization tool used for business intelligence datatypes. However, it can be used for educational data exploration as well.  

24. Plotly 

Used for interactive charts, maps, and graphs, Plotly is a great data visualization tool whose visualization products can be shared further on social media platforms. 

25. Excel 

The old-school Microsoft Exel is a data visualization tool that provides an easy interface and offers visualizations such as scatter plots, which establish relationships between datasets. 

26. IBM watson analytics 

IBM’s cloud-based investigation administration, Watson Analytics allows users to discover trends in information quickly and is among their top free tools. 

27. FushionCharts 

A product of InfoSoft Global, FusionCharts is used by nearly 80% of Fortune 500 companies across the globe. It provides over ninety diagrams and outlines that are both simple and sophisticated.  

28. Dundas BI 

This data visualization tool offers highly customizable visualization with interactive maps, charts, scorecards. Dundas BI provides a simplified way to clean, inspect, and transform large datasets by giving users full control over the visual elements.  

29. RAW 

RAW, or RawGraphs, works as a link between spreadsheets and data visualization. Providing a variety of both conventional and non-conventional layouts, RAW offers quality data security. 

30. Redash 

An open-source web application, Redas is used for database cleaning and visualizing results.  

31. Dygraphs 

A fast, open-source, Javascript-based charting library, Dygraphs allows users to interpret and explore dense data sets.  

32. RapidMiner 

A data science platform for companies, RapidMiner allows analyses of the overall impact of organizations’ employees, data, and expertise. This platform supports many analytics users.  

33. Gephi 

Among the top open-source and free visualizations and exploration softwares, Gephi provides users with all kinds of charts and graphs. It’s great for users working with graphs for simple data analysis.  

This blog highlights healthcare data exploration with Tableau’s visualization techniques. We will learn how it presents an integrated view and evidence for making healthcare decisions.

According to Statista, the amount of healthcare data generated by the end of 2020, had increased to the colossal amount of 2,314 exabytes. 

Big data analysis is booming in every industry. Similarly, modernization and achieving data are key imperatives in healthcare. Visualization provides an intuitive way to present and understand user data.  

Tableau helped the healthcare sector to optimize challenges such as during the COVID-19 crisis and pushed healthcare professionals to be more predictive in how they use their resources going forward. 

Data visualization objective in healthcare 

Medical institutes deal with big data regularly. They require extensive data handling support to interpret information and understand its implications. You must have seen the patient’s heartbeat visualization in tv series and dramas. That is one example of how significant it is to visually realize the dataset for everyone.  

Moreover, it improves the management’s decisions on healthcare policies and services by presenting an integrated view and evidence to take healthcare decisions.  

It is indeed challenging to figure out a meaningful conclusion from the above data set. Even for a medical professional, it gets tedious to read complicated data. 

In that case, how is data visualization used in healthcare? Data visualization eases the data reading task for medical assistants by simplifying the datasets. It transforms and then visually displays medical data points that synthesize the analysis of data points. As a result, it gets easier to process, visualize, and understand for the layman as well. 

Watch this event, we will cover how to design a dashboard and more in Tableau. This crash course is intended for beginners. By the end of the session, you will know: 

Crash course on designing a dashboard in Tableau 

It is important for healthcare because it can help to identify patterns, trends, and correlations between different types of data. Data visualization can also be used to make complex information easier to understand, which helps improve the quality of care. 

The upward trend of data gathered globally by healthcare professionals shows the need for advanced visualization tools to analyze and explore more efficiently.  

Use of data visualization tools for clinical assessment 

To develop high-quality visualizations, healthcare organizations will require open-source and commercial data visualization libraries, as well as open-source libraries. They will also benefit from the ability to render data sets with high performance.  

Powerful data visualization libraries:

There are several differences between the open-source and commercial data visualization libraries. Numerous open-source libraries are available to the public. These libraries provide simple, but effective data exploration.  

However, several commercial libraries are capable of processing data in real-time and can render hundreds of thousands of data points in a single render. Healthcare organizations must be prepared to visualize all of their data to create high-quality visualizations at a rapid render. 

Rendering performances:

These are available in several languages, including JavaScript, Python, and, NET. The libraries’ purposes and rendering capabilities vary. Open-source libraries are constrained in resources and perform poorly, while commercial libraries are there to resolve that issue and can render millions of data points in real-time without problems.  

Resource optimization:

The healthcare sector is committed to visualizing all its data, but is it fully prepared? It is preparing for and using GPU-accelerated libraries to deliver higher-quality visualizations at a faster render time, regardless of the health sector’s computing power. 

Using Tableau to manage health data exploration

Tableau connects users with a variety of data sources and enables them to create data visualizations by making charts, maps, dashboards, and stories through a simple drag-and-drop interface. It is possible to create a simple view to explore sample data using Tableau for beginners.  

It offers several visualization techniques including tables, maps, bar charts, heatmaps, tree maps, line charts, bubble charts, etc. Often, we require customizations in data, such as radar charts with user intention. In this scenario, it allows the users to create interactive visualizations and add engaging views to express the desired format using filters, drop-down lists, calculated fields so on.   

Read this blog to learn about how data science benefits healthcare systems 

Features offered by Tableau for healthcare professionals 

Let’s shed some light on the core healthcare features offered by Tableau to help medical institutes. 

Payer analysis:

The data about payers’ operations, plans, and claims provided by healthcare payer analytics are used to derive insights into current healthcare patterns. Also, payer analytics software drives optimal patient experiences and provides doctors with data-driven care outcomes by using the world’s leading healthcare analytics platform 

Provider analytics:

A provider data analysis monitors payment for services rendered in a facility, such as a hospital or skilled nursing facility, to ensure that duplicate payments are not being made through both a facility and professional claim submission for the same service. 

Medical device analytics:

Optimize virtual sales, improve supply chain management, and realize end-to-end business transformation with the world’s leading analytics platform. It allows health institutes to visualize patient journeys over time. 

Benefits of Tableau in different industries

Tableau is a data visualization software that is used to create interactive, informative, and data-driven graphs. The software has multiple features that make it an ideal tool for visualizing different types of data. 

 Tableau has been used by various industries including healthcare, finance, and retail. It’s also being used in the entertainment industry to visualize statistics about movies and TV shows. 

Tableau helps organizations with big data problems by making it easy to work with large amounts of information. It provides a way for people to find insights into their data without having any programming skills or knowledge of SQL.

This makes it an ideal tool for people who want to explore their data on their own without having to rely on IT experts or developers all the time. Tableau also provides a good way for companies to share their insights by making visualizations public.

In this blog, we will look into different methods of data transformation, data exploration, and data visualization using Power BI.

Prerequisites to work with Power BI: 

• Download Dataset 
• Install Power BI 

Downloading Data: 

 We will use an open-source dataset available on Kaggle. This link contains several other datasets, but we will use “states_all.csv” in this blog. The link contains all the column descriptions. 

Watch this video to learn Power BI end-to-end

Moving forward, let us first see how to install it on our desktop: 

Installing Power BI: 

You can download Power BI for any OS from here. The installation is relatively easier, you can click on next for every prompt you get. After you have installed it, let us open it. 

This will be the screen you will land on after opening it. 

The data we have is in a CSV file so, we can use “Import data from Excel” to view it in Power BI (remember to select All Files from the file explorer). Just navigate to the file and click on open. A new screen will open which will preview the data you selected. First, we need to do some transformations on this data, for that click on Transform data at the bottom right of this screen.  

Transformation: 

There are some columns that have null values, so we can remove them. We can do this by clicking on individual columns and then selecting Remove Columns from the upper tab. Do the same for other columns 

• OTHER_EXPENDITURE 
• GRADES_1_8_G 

• GRADES_9_12_G 
• AVG_READING_8_SCORE 

We can also remove the PRIMARY_KEY column as it is of no importance to us in the later steps.  

After doing all this, click on Close & Apply at the top left.  

Data visualization: 

Now we are ready to visualize the data. On the right, you can see all the imported columns from the CSV file. 

1. Clustered column chart:

Let us create a clustered column chart to visualize 4th grade scores per year. To do this first select clustered column chart from the Visualizations pane. After that, drag down the Year column to the X-axis and GRADES_4_G to the y-axis. 

As we can see from the graph above, the sum of all the grades lies in the same range every year 

2. Line chart:

Now Let us make a line chart showing local revenue affected every year. For that, we can select a line chart from the Visualizations pane. Select Year as the x-axis and LOCAL_REVENUE as the y-axis.  

From the above graph, we can see the local revenue increasing every year 

3. Pie chart:

If we want to see the Revenue generated by each; Local, Federal, and State. We can use a Pie Chart for that. We can select Pie Chart from the pane and drag LOCAL_REVENUE, FEDERAL_REVENUE and STATE_REVENUE to the values tab. 

The pie chart shows the sum of different amounts of revenue  

4. Area chart:

At last, we can compare any two grades to see their revenue changes during the past years. For this purpose, we can use the Area Chart from the visualizations pane and use GRADES_4_G as the y-axis and GRADES_12_G as the secondary y-axis. Drag YEAR to the x-axis.  

The Area chart shows the difference in grades of class 4 and 12 on top of each other. 

Finally, we have this report to showcase to our colleagues or friends. 

Conclusion: 

In this blog, we saw how to use the tool for data transformation and what are some different graphs we can use to visualize academic data. Learn more about Power BI in the course offered by Data Science Dojo and enable yourself to emulate these learnings at work. 

Power BI transforms your data into visually immersive and interactive insights. It connects your multiple sources of data with the help of apps, software services, and connectors.

Whether you save your data on an excel spreadsheet, on cloud premises, or on on-premises data warehouses, Power BI gathers and shares your data easily with anyone whenever you want. 

Who uses Power BI? 

The use of it may vary depending on the purpose you need to fulfill. Mostly, the software is used for presenting reports and viewing data dashboards and presentations. If you are responsible for creating reports, presenting weekly datasheets, or even being involved in data analysis then probably you might make extensive use of Power BI Desktop or Report Builder to create reports. Also, it allows you to publish your report to its service where you can view and share it later.   

Whereas developers use Power BI APIs to push data into datasets or to embed dashboards and reports into their own custom applications. 

Let’s learn how Power BI works step by step: 

 Loading dataset in Power BI 

On the dashboard, there are a number of options to use for uploading or importing your dataset. So, the first step is to import your dataset. The software supports a number of data reports formats that we discussed earlier. Let’s say you add an excel sheet to Power BI, for that click on excel workbook on the main screen and simply select the file you want to upload.  

As your data is visible now, first you need to perform data pre-processing which requires cleaning up your data and then transforming your data. As you click on transform data, you will be taken to the power query editor. 

Power Query Editor 

Power Query is the engine behind Power BI. All the data pre-processing is going to be done in this window. It cleans and import millions of rows into the data model to help you perform data analysis after. 

The tool is simple to use and requires no code to do any task. With the help of Power Query, it is possible to Extract, Transform, and Load the data. The tool offers the following benefits and simplify the tasks you perform regularly: 

• In order to access and transform data regularly, you enter a repeatable query that just needs to be refreshed in the future to get up to data.  
• Power Query provides connectivity to hundreds of data sources and over 350 different types of data transformations 
• Equipped with a number of pre-built transformation functions as simple as adding or deleting rows 

Build visuals with your data 

You can check out a number of Power BI visualizations that you can choose from the visualization pane. Simply choose from the range of visuals available in the panel. 

You can create custom data visualizations if you can’t find the visual you want in AppSource. To differentiate your organization and build something distinctive, personalize data visualizations. When they’re ready, you can share what you’ve created with your team or publish it to its community. 

Working with the eye-catching visuals increase comprehension, retention, and appeal that help you interact with your data and make informed decisions quickly. 

Watch this video to learn each step of developing visuals for your specific industry and business: 

Number of visualizations options offered by Power BI 

It is a data visualization and analysis tool that offers different types of visualizations. The most popular and useful ones are Charts, Maps, Tables, and Data Bars. 

Charts are a simple way to present data in an easy-to-understand format. They can be used for showing trends, comparisons or changes over time. A map is a great way to show the geographical location of certain events or how they relate to each other on a map. A table provides detailed information that can be sorted by columns and rows so it’s easier to analyze the information in the table. Data bars are used to show progress towards goals or targets with their height representing the amount of progress made. 

Career opportunities with Power BI

Power BI:

Analyst
Software Engineer
Senior Business Intelligence Analyst

Business Analyst
Data Analyst
Developer

Senior Software Engineer

Recently, the use of this tool has increased and has been adopted widely in multiple industries. It includes IT, healthcare, financial services, insurance, staffing & recruiting, and computer software. Some of the major companies that use the tool include:

Adobe (USA)
Conde Nast (USA)
Dell (USA)
Hospital Montfort (Canada)
Kraft Heinz Co (USA)
Meijer (USA)
Nestle (China)
Rolls-Royce Holdings PLC (UK)

The average annual salary of a Power BI professional in Unites States is $100,726 /yr.

Begin learning Power BI now!

The advantage of this visualization tool is its ease of use, even by people who don’t consider themselves to be very technologically proficient. As long as you have access to the data sources, the dashboard, and a working network connection, you can use it to process the information, create the necessary reports, and send them off to the right teams or individuals.

Start learning Power BI today with Data Science Dojo and excel your career

In this blog, we will discuss the key ingredients for a great chart. We will highlight the Data Science Dojo session held by Nick Desbarats.

(more…)

50 self-explanatory data science quotes by thought leaders you need to read if you’re a Data Scientist, – covering the four core components of data science landscape. 

Data science for anyone can seem scary. This made me think of developing a simpler approach to it. To reinforce a complicated idea, quotes can do wonders. Also, they are a sneak peek into the window of the author’s experience. With precise phrasing with chosen words, it reinstates a concept in your mind and offers a second thought to your beliefs and understandings.  

In this article, we jot down 51 best quotes on data science that were once shared by experts. So, before you let the fear of data science get to you, browse through the wise words of industry experts divided into four major components to get inspired. 

Data strategy 

If you successfully devise a data strategy with the information available, then it will help you to debug a business problem. It builds a connection to the data you gather and the goals you aim to achieve with it. Here are five inspiring and famous data strategy quotes by Bernard Marr from his book, “Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things” 

1. “Those companies that view data as a strategic asset are the ones that will survive and thrive.” 
2. “Doesn’t matter how much data you have, it’s whether you use it successfully that counts.” 
3. “If every business, regardless of size, is now a data business, every business, therefore, needs a robust data strategy.” 
4. “They need to develop a smart strategy that focuses on the data they really need to achieve their goals.” 
5. “Data has become one of the most important business assets, and a company without a data strategy is unlikely to get the most out of their data resources.” 

Other Best Quotes on Data Science

Some other influential data strategy quotes are as follows: 

6. “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris Lynch, Former CEO, Vertica  

7. “You can’t run a business today without data. But you also can’t let the numbers drive the car. No matter how big your company is or how far along you are, there’s an art to company-building that won’t fit in any spreadsheet.” Chris Savage, CEO, Wistia 

8. “Data science is a combination of three things: quantitative analysis (for the rigor required to understand your data), programming (to process your data and act on your insights), and narrative (to help people comprehend what the data means).” — Darshan Somashekar, Co-founder, at Unwind media 

9. “In the next two to three years, consumer data will be the most important differentiator. Whoever is able to unlock the reams of data and strategically use it will win.” — Eric McGee, VP Data and Analytics 

10. “Data science isn’t about the quantity of data but rather the quality.” — Joo Ann Lee, Data Scientist, Witmer Group 

11. “If someone reports close to a 100% accuracy, they are either lying to you, made a mistake, forecasting the future with the future, predicting something with the same thing, or rigged the problem.” — Matthew Schneider, Former United States Attorney 

12. “Executive management is more likely to invest in data initiatives when they understand the ‘why.’” — Della Shea, Vice President of Privacy and Data Governance, Symcor

13. “If you want people to make the right decisions with data, you have to get in their head in a way they understand.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

14. “Everyone has the right to use company data to grow the business. Everyone has the responsibility to safeguard the data and protect the business.” — Travis James Fell, CSPO, CDMP, – Product Manager 

15. “For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity. The more real-time and granular we can get, the more responsive, and more competitive, we can be.”  Peter Levine, VC and General Partner ,Andreessen Horowitz 

Data engineering 

Without a sophisticated system or technology to access, organize, and use the data, data science is no less than a bird without wings. Data engineering builds data pipelines and endpoints to utilize the flow of data. Check out these top quotes on data engineering by thought leaders: 

16. “Defining success with metrics that were further downstream was more effective.” John Egan, Head of Growth Engineer, Pinterest 

17. ” Wrangling data is like interrogating a prisoner. Just because you wrangled a confession doesn’t mean you wrangled the answer.” — Brad Schneider – Politician 

18. “If you have your engineering team agree to measure the output of features quarter over quarter, you will get more features built. It’s just a fact.” Jason Lemkin, Founder, SaaStr Fund 

19. “Data isn’t useful without the product context. Conversely, having only product context is not very useful without objective metrics…” Jonathan Hsu, CFO, and COO,  AppNexus & Head of Data Science, at Social Capital 

20.  “I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” Aaron Koblin, Entrepreneur in Data and Digital Technologies 

21. “Many people think of data science as a job, but it’s more accurate to think of it as a way of thinking, a means of extracting insights through the scientific method.” — Thilo Huellmann, Co-fFounder, at Levity 

22. “You want everyone to be able to look at the data and make sense out of it. It should be a value everyone has at your company, especially people interacting directly with customers. There shouldn’t be any silos where engineers translate the data before handing it over to sales or customer service. That wastes precious time.” Ben Porterfield, Founder and VP of Engineering, at Looker 

23. “Of course, hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too — and a lot of companies either don’t know how or forget to act on it.” Stewart Butterfield, CEO,   Slack

Data analysis and models 

Every business is bombarded with a plethora of data every day. When you get tons of data, analyze it and make impactful decisions. Data analysis uses statistical and logical techniques to model the use of data:.  

24. “In most cases, you can’t build high-quality predictive models with just internal data.” — Asif Syed, Vice President of Data Strategy, Hartford Steam Boiler 

25. “Since most of the world’s data is unstructured, an ability to analyze and act on it presents a big opportunity.” — Michael Shulman, Head of Machine Learning, Kensho 

26. “It’s easy to lie with statistics. It’s hard to tell the truth without statistics.” — Andrejs Dunkels, Mathematician, and Writer 

27. “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard, Senior Vice President, Gartner Research 

28. “Use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction.” Mitch Lowe, Co-founder of Netflix 

29. “Your metrics influence each other. You need to monitor how. Don’t just measure which clicks generate orders. Back it up and break it down. Follow users from their very first point of contact with you to their behavior on your site and the actual transaction. You have to make the linkage all the way through.” Lloyd Tabb, Founder, Looker 

30. “Don’t let shallow analysis of data that happens to be cheap/easy/fast to collect nudge you off-course in your entrepreneurial pursuits.” Andrew Chen, Partner at Andreessen Horowitz,  

31. “Our real job with data is to better understand these very human stories, so we can better serve these people. Every goal your business has is directly tied to your success in understanding and serving people.” — Daniel Burstein, Senior Director, Content & Marketing, Marketing Sherpa 

32. “A data scientist combines hacking, statistics, and machine learning to collect, scrub, examine, model, and understand data. Data scientists are not only skilled at working with data, but they also value data as a premium product.” — Erwin Caniba, Founder and Owner,Digitacular Marketing Solutions 

33. “It has therefore become a strategic priority for visionary business leaders to unlock data and integrate it with cloud-based BI and analytic tools.” — Gil Peleg, Founder , Model 9 – Crunchbase 

34.  “The role of data analytics in an organization is to provide a greater level of specificity to discussion.” — Jeff Zeanah, Analytics Consultant  

35. “Data is the nutrition of artificial intelligence. When an AI eats junk food, it’s not going to perform very well.” — Matthew Emerick, Data Quality Analyst 

36. “Analytics software is uniquely leveraged. Most software can optimize existing processes, but analytics (done right) should generate insights that bring to life whole new initiatives. It should change what you do, not just how you do it.”  Matin Movassate, Founder, Heap Analytics 

37. “No major multinational organization can ever expect to clean up all of its data – it’s a never-ending journey. Instead, knowing which data sources feed your BI apps, and the accuracy of data coming from each source, is critical.” — Mike Dragan, COO, – Oveit 

38. “All analytics models do well at what they are biased to look for.” — Matthew Schneider, Strategic Adviser 

39. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” Geoffrey Moore, Author and Consultant 

Data visualization and operationalization 

When you plan to take action with your data, you visualize it on a very large canvas. For an actionable insight, you must squeeze the meaning out of all the analysis performed on that data, this is data visualization. Some  data visualization quotes that might interest you are: 

40. “Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.” — Prashanth Southekal, Business Analytics Author 

41. “Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, Adjunct Professor, MIT 

42. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, Author, “Effective Data Storytelling” 

43. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

44. The goal is to turn data into information and information into insight. Carly Fiorina, Former CEO, Hewlett Packard 

45. “Data reveals impact, and with data, you can bring more science to your decisions.” Matt Trifiro, CMO, at Vapor IO 

46. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, data strategy consultant and author, “Effective Data Storytelling” 

47. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

48. “One cannot create a mosaic without the hard small marble bits known as ‘facts’ or ‘data’; what matters, however, is not so much the individual bits as the sequential patterns into which you organize them, then break them up and reorganize them'” — Timothy Robinson, Physician Scientist 

49. “Data are just summaries of thousands of stories–tell a few of those stories to help make the data meaningful.” Chip and Dan Heath, Authors of Made to Stick and Switch

Parting thoughts on amazing data science quotes

Each quote by industry experts or experienced professionals provides us with insights to better understand the subject. Here are the final quotes for both aspiring and existing data scientists: 

50. “The self-taught, un-credentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.” – Neil Raden, Founder, and Principal Analyst, Hired Brains Research. 

51. “Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – Mike Loukides, Editor, O’Reilly Media. 

Have we missed any of your favorite quotes on data? Or do you have any thoughts on the data quotes shared above? Let us know in the comments.

The current world relies on data visualization for things to run smoothly. There have been multiple research projects on nonverbal communication and many researchers came to comparable results that 93% of all communication is nonverbal. Whether you are scrolling on social media or watching television, you are consuming data. Data scientists strongly believe that data can create or break your business brand.  

The concept of content marketing strategy requires you to have a unique operating model to attain your business objective. Remember that everybody is busy, and no one has time to read dull content on the internet.

This is where the art of data visualization comes in to help the dreams of many digital marketers come true. Below are some practical data visualization techniques that you can use to supercharge your content strategy!  

1. Invest in accurate data  

Everybody loves to read the information they can rely on and use in decision-making. When you present data to your audience in the form of visualization make sure the data is accurate and mention its source to gain the trust of your audience. You need to ensure that all the information you have is highly accurate and can be utilized in decision-making.  

If your business brand presents inaccurate data, you are likely to lose many potential clients who depend on your Company. Obviously, customers are likely to come and view your visual content, but they won’t be happy because your data is inaccurate. Remember that there is no harm in gathering information from a third-party source. You only need to ensure that the information is accurate.

According to the ERP-information data can never be 100% accurate but it can be more or less accurate depending on how close it adheres to reality. The closer that data sticks to reality, the higher its accuracy.  

2. Use real-time data to be unique  

Posting real-time data is an excellent way of attracting a significant number of potential customers. Many people opt for brands that present data on time, depending on the market situation. This strategy proved to be efficient during the black Friday season, whereby companies recorded a significant number of sales within the shortest time.  

In addition, real-time data plays a critical role in building trust between a brand and its customers. When customers realize that you are posting things that are just happening, their level of true skyrockets. 

3. Create a story 

Once you have decided about including visual content in your content strategy, you also need to find out an exciting story that the visual will present to the audience. Before you start authoring the story, think about the ins and outs of your content to ensure that you have nailed everything in your head.  

You can check out the types of visual content that have been created by some of the big brands on the internet. Try to mimic how these brands present their stories to the audience.  

4. Promote visualizations perfectly 

Promoting imagery content does not mean that you need to spend the whole day working on a single visual. Create simpler and more interactive excel charts (Bar chart, Line chart, Sankey diagram, and Box and Whisker Plot, etc.) to encourage your audience. This is not what promoting means! It means that you need to communicate to your audience directly through different social media platforms.  

Also, you can opt to send direct emails, given the fact that you have their contact details. The ultimate goal of this campaign is to make your visual go viral across the internet and reach as many people as possible. Ensure that you know your target audience to make your efforts yield profit.  

5. Gather and present unique data  

Representation of data plays a fundamental role when developing a unique identity for your brand.  You have the power to use visuals to make your brand stand out from your competitors. Collecting and presenting unique data gives you an added advantage in business that makes you unique. 

To achieve this level of big data, you need to conduct in-depth research and dig down across different variables to find unique data. Even though it may sound simple, this is not the case. Also, selecting big data is simple, but the complexity comes with selecting the most appropriate data points.  

6. Know your audience 

Getting to know your audience is a fundamental aspect that you should always consider. It gives you detailed insights not about understanding the nature of your content but also about promoting your visualization. To be able to encourage your visualization ideally, you need to understand your audience. 

When designing different visualization types, you should also channel all your eyes to the platform you are targeting. Decide on the media where you are sharing various types of content depending on the nature of the audience available on the respective platforms. 

7. Understand your craft 

Conduct in-depth research to understand what works for you and what doesn’t work. For instance, one of the benefits of data visualization is that it reduces the time it takes to read through loads of content. If you are mainly writing content for your readers to share across the market audience, a maximum of two hundred and thirty words is enough. 

It is an art and science that requires you to conduct remarkable research to uncover essential information. Once you uncover the necessary information, you will definitely get to know your craft.  

8. Learn from the best  

The digital marketing world involves continuous learning to remain at the top of the game. The best way to learn in business is to monitor what the developed brands are doing to succeed. You can learn the content strategy used by international companies such as Netflix to get a test of what it means to promote your brand across its target market.  

 9. Gather the respective data visualization tool 

After conducting your research and settling on a story that reciprocates your brand, you have to gather the Respective tools necessary to generate the story you need. You would acquire creative tools with a successful track record of developing quality output. 

There are multiple data visualization tools on the web that you can choose and use. However, some people recommend starting from scratch, depending on the nature of the output they want. Some famous data visualization tools are Tableau, Microsoft Excel, Power BI, ChartExpo, and Plotly.  

10. Research and testing  

Do not forget about the power of research and testing. Acquire different tools to help you conduct research and test different elements to check if they can work and generate the desired results. You should be keen to analyze what can work for your business and what cannot.  

Need for data visualization

The business world is in dire need of representing data to enhance competitive content strategies. A study done by the Wharton School of Business has revealed that appealing visuals of  complex data can shorten a business meeting by 24% since all the essential elements are outlined clearly. However, to grab the attention of your target market, you need to come up with something unique to be successful. 

The content of this blog is based on examples/notes/experiments related to the material presented in the “Building Data Visualization Tools” module of the “Mastering Software Development in R” Specialization (Coursera) created by Johns Hopkins University [1].

Required data visualization packages

• ggplot2, a system for “declaratively” creating graphics, based on “The Grammar of Graphics.”
• gridExtra, provides a number of user-level functions to work with “grid” graphics.
• dplyr, a tool for working with data frame-like objects, both in and out of memory.
• viridis, the Viridis color palette.
• ggmap, a collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps)

  # If necessary to install a package run
  # install.packages("packageName")
  
  # Load packages
  library(ggplot2)
  library(gridExtra)
  library(dplyr)
  library(viridis)
  library(ggmap)
  

Data

The ggplot2 package includes some datasets with geographic information. The ggplot2::map_data() function allows to get map data from the maps package (use ?map_data form more information).

Specifically the <code class="highlighter-rouge">italy dataset [2] is used for some of the examples below. Please note that this dataset was prepared around 1989 so it is out of date, especially information pertaining to provinces (see ?maps::italy).

# Get the italy dataset from ggplot2
# Consider only the following provinces "Bergamo" , "Como", "Lecco", "Milano", "Varese"
# and arrange by group and order (ascending order)
italy_map <- ggplot2::map_data(map = "italy")
italy_map_subset <- italy_map %>%
filter(region %in% c("Bergamo" , "Como", "Lecco", "Milano", "Varese")) %>%
arrange(group, order)

Each observation in the dataframe defines a geographical point with some extra information:

• long & lat, longitude and latitude of the geographical point
• group, an identifier connected with the specific polygon points are part of – a map can be made of different polygons (e.g. one polygon for the mainland and one for each island, one polygon for each state, …)
• order, the order of the point within the specific group– how all of the points are part of the same group should be connected in order to create the polygon
• region, the name of the province (Italy) or state (USA)

  head(italy_map, 3)
  ##       long      lat group order        region subregion
  ## 1 11.83295 46.50011     1     1 Bolzano-Bozen      
  ## 2 11.81089 46.52784     1     2 Bolzano-Bozen      
  ## 3 11.73068 46.51890     1     3 Bolzano-Bozen
  

How to work with maps

Having spatial information in the data gives the opportunity to map the data or, in other words, visualizing the information contained in the data in a geographical context. R has different possibilities to map data, from normal plots using longitude/latitude as x/y to more complex spatial data objects (e.g. shapefiles).

Mapping with ggplot2 package

The most basic way to create maps with your data is to use ggplot2, create a ggplot object and then, add a specific geom mapping longitude to x aesthetic and latitude to y aesthetic [4] [5]. This simple approach can be used to:

• create maps of geographical areas (states, country, etc.)
• map locations as points, lines, etc.

Create a map showing “Bergamo,” Como,” “Varese,” and “Milano” provinces in Italy using simple points…

When plotting simple points the geom_point function is used. In this case the polygon and order of the points is not important when plotting.

italy_map_subset %>%
 ggplot(aes(x = long, y = lat)) +
geom_point(aes(color = region))

Create a map showing “Bergamo,” Como,” “Varese,” and “Milano” provinces in Italy using lines…

The geom_path function is used to create such plots. From the R documentation, geom_path “… connects the observation in the order in which they appear in the data.” When plotting using geom_path is important to consider the polygon and the order within the polygon for each point in the map.

The points in the dataset are grouped by region and ordered by order. If information about the region is not provided then the sequential order of the observations will be the order used to connect the points and, for this reason, “unexpected” lines will be drawn when moving from one region to the other.

On the other hand if information about the region is provided using the group or color aesthetic, mapping to region, the “unexpected” lines are removed (see example below).

plot_1 <- italy_map_subset %>%
 ggplot(aes(x = long, y = lat)) +
geom_path() +
ggtitle("No mapping with 'region', unexpected lines")

plot_2 <- italy_map_subset %>%
 ggplot(aes(x = long, y = lat)) +
 geom_path(aes(group = region)) +
ggtitle("With 'group' mapping")

plot_3 <- italy_map_subset %>%
 ggplot(aes(x = long, y = lat)) +
geom_path(aes(color = region)) +
ggtitle("With 'color' mapping")

grid.arrange(plot_1, plot_2, plot_3, ncol = 2, layout_matrix = rbind(c(1,1), c(2,3)))

Mapping with ggplot2 is possible to create more sophisticated maps like choropleth maps [3]. The example below, extracted from [1], shows how to visualize the percentage of Republican votes in 1976 by states.

# Get the USA/ state map from ggplot2
us_map <- ggplot2::map_data("state")

# Use the 'votes.repub' dataset (maps package), containing the percentage of
# republican votes in the 1900 elections by state. Note
# - the dataset is a matrix so it needs to be converted to a dataframe
# - the row name defines the relevant state

votes.repub %>%
tbl_df() %>%
mutate(state = rownames(votes.repub), state = tolower(state)) %>%
 right_join(us_map, by = c("state" = "region")) %>%
ggplot(mapping = aes(x = long, y = lat, group = group, fill = `1976`)) +
geom_polygon(color = "black") +
theme_void() +
scale_fill_viridis(name = "RepublicannVotes (%)")

Maps with ggmap package, Google Maps API and others

Another way to create maps is to use the ggmap[4] package (see Google Maps API Terms of Service). As stated in the package description…

“A collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps). It includes tools common to those tasks, including functions for geolocation and routing.” R Documentation

The package allows to create/plot maps using Google Maps and few other service providers, and perform some other interesting tasks like geocoding, routing, distance calculation, etc. The maps are actually ggplot objects making possible to reuse the ggplot2 functionality like adding layers, modify the theme, etc…

“The basic idea driving ggmap is to take a downloaded map image, plot it as a context layer using ggplot2, and then plot additional content layers of data, statistics, or models on top of the map. In ggmap this process is broken into two pieces – (1) downloading the images and formatting them for plotting, done with get_map, and (2) making the plot, done with ggmap. qmap marries these two functions for quick map plotting (c.f. ggplot2’s ggplot), and qmplot attempts to wrap up the entire plotting process into one simple command (c.f. ggplot2’s qplot).” [4]

How to create and plot a map…

The ggmap::get_mapfunction is used to get a base map (a ggmap object, a raster object) from different service providers like Google Maps, OpenStreetMap, Stamen Maps or Naver Maps (default setting is Google Maps). Once the base map is available, then it can been plotted using the ggmap::ggmap function. Alternatively the ggmap::qmap function (quick map plot) can be used.

# When querying for a base map the location must be provided
# name, address (geocoding)
# longitude/latitude pair
base_map <- get_map(location = "Varese")
ggmap(base_map) + ggtitle("Varese")

# qmap is a wrapper for
# `ggmap::get_map` and `ggmap::ggmap` functions.
qmap("Varese") + ggtitle("Varese - qmap")

How to change the zoom in the map…

The zoom argument (default value is auto) in ggmap::get_map the function can be used to control the zoom of the returned base map (see ?get_map for more information). Please note that the possible values/range for the zoom argument changes with the different sources.

# An example using Google Maps as a source
# Zoom is an integer between 3 - 21 where
# zoom = 3 (continent)
# zoom = 10 (city)
# zoom = 21 (building)

base_map_10 <- get_map(location = "Varese", zoom = 10)
base_map_18 <- get_map(location = "Varese", zoom = 16)

grid.arrange(ggmap(base_map_10) + ggtitle("Varese, zoom 10"),
         ggmap(base_map_18) + ggtitle("Varese, zoom 18"),
         nrow = 1)

How to change the type of map…

The maptype argument in ggmap::get_map the function can be used to change the type of map aka map theme. Based on the R documentation (see ?get_map for more information)

‘[maptype]… options available are “terrain”, “terrain-background”, “satellite”, “roadmap”, and “hybrid” (google maps), “terrain”, “watercolor”, and “toner” (stamen maps)…’.

# An example using Google Maps as a source
# and different map types

base_map_ter <- get_map(location = "Varese", maptype = "terrain")
base_map_sat <- get_map(location = "Varese", maptype = "satellite")
base_map_roa <- get_map(location = "Varese", maptype = "roadmap")

grid.arrange(ggmap(base_map_ter) + ggtitle("Terrain"),
         ggmap(base_map_sat) + ggtitle("Satellite"),
         ggmap(base_map_roa) + ggtitle("Road"),
         nrow = 1)

How to change the source for maps…

While the default source for maps with ggmap::get_map is Google Maps, it is possible to change the map service using the source argument. The supported map services/sources are Google Maps, OpenStreeMaps, Stamen Maps, and CloudMade Maps (see ?get_map for more information).

# An example using different map services as a source

base_map_google <- get_map(location = "Varese", source = "google", maptype = "terrain")
 base_map_stamen <- get_map(location = "Varese", source = "stamen", maptype = "terrain")

grid.arrange(ggmap(base_map_google) + ggtitle("Google Maps"),
         ggmap(base_map_stamen) + ggtitle("Stamen Maps"),
         nrow = 1)

How to geocode a location…

The ggmap::geocode function can be used to find latitude and longitude of a location based on its name (see ?geocode for more information). Note that Google Maps API limits the possible number of queries per day, geocodeQueryCheck can be used to determine how many queries are left.

# Geocode a city
geocode("Sesto Calende")
##        lon     lat
## 1 8.636597 45.7307
# Geocode a set of cities
geocode(c("Varese", "Milano"))
##        lon     lat
## 1 8.825058 45.8206
## 2 9.189982 45.4642

# Geocode a location
geocode(c("Milano", "Duomo di Milano"))
##        lon     lat
## 1 9.189982 45.4642
## 2 9.191926 45.4641
geocode(c("Roma", "Colosseo"))
##        lon      lat
## 1 12.49637 41.90278
## 2 12.49223 41.89021

How to find a route between two locations…

The ggmap::route function can be used to find a route from Google using different possible modes, e.g. walking, driving, … (see ?ggmap::route for more information).

“The route function provides the map distances for the sequence of “legs” which constitute a route between two locations. Each leg has a beginning and ending longitude/latitude coordinate along with a distance and duration in the same units as reported by mapdist. The collection of legs in sequence constitutes a single route (path) most easily plotted with geom_leg, a new exported ggplot2 geom…” [4]

route_df <- route(from = "Somma Lombardo", to = "Sesto Calende", mode = "driving")
head(route_df)
##      m    km     miles seconds   minutes       hours startLon startLat
## 1  198 0.198 0.1230372      52 0.8666667 0.014444444 8.706770 45.68277
 ## 2  915 0.915 0.5685810     116 1.9333333 0.032222222 8.705170 45.68141
## 3  900 0.900 0.5592600      84 1.4000000 0.023333333 8.702070 45.68835
## 4 5494 5.494 3.4139716     390 6.5000000 0.108333333 8.691054 45.69019
## 5  205 0.205 0.1273870      35 0.5833333 0.009722222 8.648636 45.72250
## 6  207 0.207 0.1286298      25 0.4166667 0.006944444 8.649884 45.72396
##     endLon   endLat leg
## 1 8.705170 45.68141   1
## 2 8.702070 45.68835   2
## 3 8.691054 45.69019   3
## 4 8.648636 45.72250   4
## 5 8.649884 45.72396   5
## 6 8.652509 45.72367   6

route_df <- route(from = "Via Gerolamo Fontana 32, Somma Lombardo",
              to = "Town Hall, Somma Lombardo", mode = "walking")

qmap("Somma Lombardo", zoom = 16) +
 geom_leg(
aes(x = startLon, xend = endLon, y = startLat, yend = endLat),  colour = "red",
size = 1.5, alpha = .5,
data = route_df) +
 geom_point(aes(x = startLon, y = startLat), data = route_df) +
geom_point(aes(x = endLon, y = endLat), data = route_df)

How to find the distance between two locations…

The ggmap::mapdist function can be used to compute the distance between two location using different possible modes, e.g. walking, driving, … (see ?ggmap::mapdist for more information).

Pro tip: Learn to use data to drive decision making

More on mapping

• Using the choroplethr and choroplethrMaps packages, see “Mapping US counties and states” section in [1]
• Working with spatial objects and shapefiles, see “More advanced mapping – Spatial objects” section in [1]
• Using htmlWidgets for mapping in R using leaflet [5]

References

[1] Peng, R. D., Kross, S., & Anderson, B. (2016). Lean Publishing.

[2] Unesco. (1987). [Italy Map]. Unpublished raw data.

[3] Choropleth map. (2017, October 17).

[4] Kahle, D., & Wickham, H. (2013). Ggmap: Spatial Visualization with ggplot2. The R Journal,5(1), 144-161.

[5] Agafonkin, V. (2010). RStudio, Inc. Leaflet for R. Retrieved from https://rstudio.github.io/leaflet/

[6] Paracchini, P. L. (2017, July 05). Building Data Visualization Tools: basic plotting with R and ggplot2.

[7] Paracchini, P. L. (2017, July 14). Building Data Visualization Tools: ‘ggplot2’, essential concepts.

[8] Paracchini, P. L. (2017, July 18). Building Data Visualization Tools: guidelines for good plots.

What is data visualization?

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your visualization skills.

In this blog, we discussed the applications of AI in healthcare. We took a deep dive into an application of AI, and prognosis prediction using an exercise. We made a simple prognosis detector with an explanation of each step. Our predictor takes symptoms as inputs and predicts the prognosis using a classification model.

The role of data science and AI (Artificial Intelligence) in the Healthcare industry is not limited to predicting and tracking disease spread. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.

If you have not already, please go back and read AI & Healthcare. If you have already read it, you will remember I wrote, “Predictive analysis, using historical data to find patterns and predict future outcomes can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data.”

This tutorial will do just that: Predict the prognosis with symptoms as our input.

Exercise: Predict prognosis using symptoms as input

Let us start by importing all the libraries needed in the exercise. We import pandas as we will be reading CSV files as Data Frame. We are importing Label Encoder from sklearn.preprocessing package. Label Encoder is a utility class to convert non-numerical labels to numerical labels. In this exercise, we predict prognosis using symptoms, so it is a classification task.

We are using RandomForestClassifier, which consists of many individual decision trees that work as an ensemble. Learn more about RandomForestClassifier by enrolling in our Data Science Bootcamp, a remote instructor-led Bootcamp. We also require classification reports and accuracy score metrics to measure the model’s performance.

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

Read CSV files

We are using this Kaggle dataset for our exercise.

It has two files, Training.csv and Testing.csv, containing training and testing data, respectively. You can download these files by going to the data section of the above link.

Read CSV files into Data Frame using pandas read_csv() function. It reads comma-separated files at supplied file path into DataFrame. It takes a file path as a parameter, so provide the right file path where you have downloaded the files.

train = pd.read_csv("File path of Training.csv")
test = pd.read_csv("File path of Testing.csv")

Check samples of the training dataset

To check what the data looks like, let us grab the first five rows of the DataFrame using the head() function.

We have 133 features. We want to predict prognosis so that it would be our target variable. The rest of the 132 features are symptoms that a person experience. The classifier would use these 132 symptoms feature to predict prognosis.

train.head()

The training set holds 4920 samples and 133 features, as shown by the shape attribute of the DataFrame.

train.shape

Output
(4920, 133)

Descriptive analysis

Description of the data in the DataFrame can be seen by describe() method of the DataFrame. We see no missing values in our DataFrame as the count of all the features is 4920, which is also the number of samples in our DataFrame. We also see that all the numeric features are binary and have a value of either 1 or 0.

train.describe()

train.describe(include=['object'])

Our target variable prognosis has 41 unique values, so there are 41 diseases in which the model will classify input. There are 120 samples for each unique prognoses in our dataset.

train['prognosis'].value_counts()

There are 132 symptoms in our dataset. The names of the symptoms will be listed if we use this code block.

possible_symptoms = train[train.columns.difference(['prognosis'])].columnsprint(list(possible_symptoms))

Output
['abdominal_pain', 'abnormal_menstruation', 'acidity', 'acute_liver_failure', 'altered_sensorium', 'anxiety', 'back_pain', 'belly_pain', 'blackheads', 'bladder_discomfort', 'blister', 'blood_in_sputum', 'bloody_stool', 'blurred_and_distorted_vision', 'breathlessness', 'brittle_nails', 'bruising', 'burning_micturition', 'chest_pain', 'chills', 'cold_hands_and_feets', 'coma', 'congestion', 'constipation', 'continuous_feel_of_urine', 'continuous_sneezing', 'cough', 'cramps', 'dark_urine', 'dehydration', 'depression', 'diarrhoea', 'dischromic _patches', 'distention_of_abdomen', 'dizziness', 'drying_and_tingling_lips', 'enlarged_thyroid', 'excessive_hunger', 'extra_marital_contacts', 'family_history', 'fast_heart_rate', 'fatigue', 'fluid_overload', 'fluid_overload.1', 'foul_smell_of urine', 'headache', 'high_fever', 'hip_joint_pain', 'history_of_alcohol_consumption', 'increased_appetite', 'indigestion', 'inflammatory_nails', 'internal_itching', 'irregular_sugar_level', 'irritability', 'irritation_in_anus', 'itching', 'joint_pain', 'knee_pain', 'lack_of_concentration', 'lethargy', 'loss_of_appetite', 'loss_of_balance', 'loss_of_smell', 'malaise', 'mild_fever', 'mood_swings', 'movement_stiffness', 'mucoid_sputum', 'muscle_pain', 'muscle_wasting', 'muscle_weakness', 'nausea', 'neck_pain', 'nodal_skin_eruptions', 'obesity', 'pain_behind_the_eyes', 'pain_during_bowel_movements', 'pain_in_anal_region', 'painful_walking', 'palpitations', 'passage_of_gases', 'patches_in_throat', 'phlegm', 'polyuria', 'prominent_veins_on_calf', 'puffy_face_and_eyes', 'pus_filled_pimples', 'receiving_blood_transfusion', 'receiving_unsterile_injections', 'red_sore_around_nose', 'red_spots_over_body', 'redness_of_eyes', 'restlessness', 'runny_nose', 'rusty_sputum', 'scurring', 'shivering', 'silver_like_dusting', 'sinus_pressure', 'skin_peeling', 'skin_rash', 'slurred_speech', 'small_dents_in_nails', 'spinning_movements', 'spotting_ urination', 'stiff_neck', 'stomach_bleeding', 'stomach_pain', 'sunken_eyes', 'sweating', 'swelled_lymph_nodes', 'swelling_joints', 'swelling_of_stomach', 'swollen_blood_vessels', 'swollen_extremeties', 'swollen_legs', 'throat_irritation', 'toxic_look_(typhos)', 'ulcers_on_tongue', 'unsteadiness', 'visual_disturbances', 'vomiting', 'watering_from_eyes', 'weakness_in_limbs', 'weakness_of_one_body_side', 'weight_gain', 'weight_loss', 'yellow_crust_ooze', 'yellow_urine', 'yellowing_of_eyes', 'yellowish_skin']

There are 41 unique prognoses in our dataset. The name of all prognoses will be listed if we use this code block:

list(train['prognosis'].unique())

Output
['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction','Peptic ulcer diseae','AIDS','Diabetes ','Gastroenteritis','Bronchial Asthma','Hypertension ','Migraine','Cervical spondylosis','Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A','Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis','Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)','Heart attack','Varicose veins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis','Arthritis','(vertigo) Paroymsal  Positional Vertigo','Acne','Urinary tract infection','Psoriasis','Impetigo']

Data visualization

new_df = train[train.columns.difference(['prognosis'])]
#Maximum Symptoms present for a Prognosis are 17
new_df.sum(axis=1).max()
Minimum Symptoms present for a Prognosis are 3
new_df.sum(axis=1).min()
series = new_df.sum(axis=0).nlargest(n=15)
pd.DataFrame(series, columns=["Occurance"]).loc[::-1, :].plot(kind="barh")

Fatigue and vomiting are the symptoms most often seen.

Encode object prognosis

Our target variable is categorical features. Let us create an instance of Label Encoder and fit it with the prognosis column of train data and test data. It will encode all possible categorical values in numerical values.

label_encoder = LabelEncoder()
label_encoder.fit(pd.concat([train['prognosis'], test['prognosis']]))

It concludes the data preparation step. Now, we can move on to model training with this data.

Training and evaluating model

Let us train a RandomForestClassifier with the prepared data. We initialize RandomForestClassifier, fit the features and label in it then finally make a prediction on our test data.

In the end, we transform label encoded prognosis values back to the original form using the fit_transform() method of the LabelEncoder object.

random_forest = RandomForestClassifier()
random_forest.fit(train[train.columns.difference(['prognosis'])], label_encoder.fit_transform(train['prognosis']))
y_pred = random_forest.predict(test[test.columns.difference(['prognosis'])])
y_true = label_encoder.fit_transform(test['prognosis'])
print("Accuracy:", accuracy_score(y_true, y_pred))
print(classification_report(y_true, y_pred, target_names=test['prognosis']))

Predict prognosis by taking symptoms as input

We have our model trained and ready to make predictions. We need to create a function that takes symptoms as input and predicts the prognosis as output. The function predict_prognosis() below is just doing that.

We take input features as a string of symptoms separated by space. We strip the string to remove spaces at the beginning and end of the string. We split this string and created a list of symptoms. We cannot use this list directly in the model for prediction as it contains symptoms’ names, but our model takes a list of 0 and 1 for the absence and presence of symptoms. Finally, with the features in the desired form, we predict the prognosis and print the predicted prognosis.

def predict_prognosis():
  print("List of possible Symptoms you can enter: ", list(train[train.columns.difference(['prognosis'])].columns))
  input_symptoms = list(input("\nEnter symptoms space separated: ").strip().split())
  print(input_symptoms)
  test_value = []
  for symptom in train[train.columns.difference(['prognosis'])].columns:
    if symptom in input_symptoms:
      test_value.append(1)
    else:
      test_value.append(0)
    np_test = np.array(test_value).reshape(1, -1)
    encoded_label = random_forest.predict(np_test)
  predicted_label = label_encoder.inverse_transform(encoded_label)[0]
  print("Predicted Prognosis: ", predicted_label)
predict_prognosis()

Give input symptoms:

Predicted prognoses

Conclusion

To sum up, we discussed the applications of AI in healthcare. Took a deep dive into an application of AI, and prognosis prediction using an exercise. Created a prognosis predictor with an explanation of each step. Finally, we tested our predictor by giving it input symptoms and got the prognosis as output.

There is so much to explore when it comes to spatial visualization using Python’s Folium library.

Spatial visualization

For problems related to crime mapping, housing prices, or travel route optimization, spatial visualization could be the most resourceful tool for getting a glimpse of how the instances are geographically located. This is beneficial as we are getting massive amounts of data from several sources, such as cellphones, smartwatches, trackers, etc. In this case, patterns and correlations, which otherwise might go unrecognized, can be extracted visually.

This blog will attempt to show you the potential of spatial visualization using the Folium library with Python. This tutorial will give you insights into the most important visualization tools that are extremely useful while analyzing spatial data.

Introduction to folium

Folium is an incredible library that allows you to build Leaflet maps. Using latitude and longitude points, Folium can allow you to create a map of any location in the world. Furthermore, Folium creates interactive maps that may allow you to zoom in and out after the map is rendered.

We’ll get some hands-on practice building a few maps using the Seattle Real-time Fire 911 Calls dataset. This dataset provides Seattle Fire Department 911 dispatches, and every instance of this dataset provides information about the address, location, date/time, and type of emergency of a particular incident. It’s extensive, and we’ll limit the dataset to a few emergency types for the purpose of explanation.

Let’s begin

Folium can be downloaded using the following commands:.

Using pip:

$ pip install folium

Using conda:

$ conda install -c conda-forge folium

Start by importing the required libraries.

import pandas as pd
import numpy as np
import folium

Let us now create an object named ‘seattle_map’ which is defined as a folium.Map object. We can add other folium objects on top of the folium.Map to improve the map rendered. The map has been centered to the longitude and latitude points in the location parameters. The zoom parameter sets the magnification level for the map that’s going to be rendered. Moreover, we have also set the tiles parameter to ‘OpenStreetMap’ which is the default tile for this parameter. You can explore more tiles such as StamenTerrain or Mapbox Control in Folium‘s documentation.

seattle_map = folium. Map
(location = [47.6062, -122.3321],
tiles = 'OpenStreetMap',
 zoom_start = 11)
seattle_map

We can observe the map rendered above. Let’s create another map object with a different tile and zoom_level. Through the ‘Stamen Terrain’ tile, we can visualize the terrain data, which can be used for several important applications.

We’ve also inserted a folium. Marker to our ‘seattle_map2’ map object below. The marker can be placed at any location specified in the square brackets. The string mentioned in the popup parameter will be displayed once the marker is clicked, as shown below.

seattle_map2 = folium. Map
(location=[47.6062, -122.3321],
    tiles = 'Stamen Terrain',
    zoom_start = 10)
#inserting marker
folium.Marker(
    [47.6740, -122.1215],
    popup = 'Redmond'
).add_to(seattle_map2)
seattle_map2

We are interested to use the Seattle 911 calls dataset to visualize the 911 calls in the year 2019 only. We are also limiting the emergency types to 3 specific emergencies that took place during this time.

We will now import our dataset, which is available through this link (in CSV format). The dataset is huge, therefore, we’ll only import the first 10,000 rows using the Pandasread_csv method. We’ll use the head method to display the first 5 rows.

(This process will take some time because the data-set is huge. Alternatively, you can download it to your local machine and then insert the file path below)

path = "https://data.seattle.gov/api/views/kzjm-xkqj/rows.csv?accessType=DOWNLOAD"
seattle911 = pd.read_csv(path, nrows = 10000)
seattle911.head()

Using the code below, we’ll convert the datatype of our Datetime variable to Date-time format and extract the year, removing all other instances that occurred before 2019.

seattle911['Datetime'] = pd.to_datetime(seattle911['Datetime'], 
                                        format='%m/%d/%Y %H:%M', utc=True)
seattle911['Year'] = pd.DatetimeIndex(seattle911['Datetime']).year
seattle911 = seattle911[seattle911.Year == 2019]

We’ll now limit the Emergency type to ‘Aid Response Yellow’, ‘Auto Fire Alarm’ and ‘MVI – Motor Vehicle Incident’. The remaining instances will be removed from the ‘seattle911’ dataframe.

seattle911 = seattle911[seattle911.Type.isin(['Aid Response Yellow', 
                                              'Auto Fire Alarm', 
                                              'MVI - Motor Vehicle Incident'])]

We’ll remove any instance that has a missing longitude or latitude coordinate. Without these values, the particular instance cannot be visualized and will cause an error while rendering.

#drop rows with missing latitude/longitude values
seattle911.dropna(subset = ['Longitude', 'Latitude'], inplace = True)

seattle911.head()

Now let’s step towards the most interesting part. We’ll map all the instances onto the map object we created above, ‘seattle_map’. Using the code below, we’ll loop over all our instances up to the length of the dataframe. Following this, we will create a folium.CircleMarker (which is similar to the folium.Marker we added above). We’ll assign the latitude and longitude coordinates to the location parameter for each instance. The radius of the circle has been assigned to 3, whereas the popup will display the address of the particular instance.

As you can notice, the color of the circle depends on the emergency type. We will now render our map.

for i in range(len(seattle911)):

    folium.CircleMarker( location = [seattle911.Latitude.iloc[i], seattle911.Longitude.iloc[i]],
        radius = 3,
        popup = seattle911.Address.iloc[i],
        color = '#3186cc' if seattle911.Type.iloc[i] == 'Aid Response Yellow' else '#6ccc31' 
        if seattle911.Type.iloc[i] =='Auto Fire Alarm' else '#ac31cc',).add_to(seattle_map) 
seattle_map

Voila! The map above gives us insights about where and what emergencies took place across Seattle during 2019. This can be extremely helpful for the local government to more efficiently place its emergency combat resources.

Advanced features provided by folium

Let us now move towards the slightly advanced features provided by Folium. For this, we will use the National Obesity by State dataset which is also hosted on data.gov. There are 2 types of files we’ll be using, a csv file containing the list of all states and the percentage of obesity in each state, and a geojson file (based on JSON) that contains geographical features in form of polygons.

Before using our dataset, we’ll create a new folium.map object with location parameters including coordinates to center the US on the map, whereas, we’ve set the ‘zoom_start’ level to 4 to visualize all the states.

usa_map = folium.Map(
    location=[37.0902, -95.7129],
    tiles = 'Mapbox Bright',
    zoom_start = 4)
usa_map

We will assign the URLs of our datasets to ‘obesity_link’ and ‘state_boundaries’ variables, respectively.

obesity_link = 'http://data-lakecountyil.opendata.arcgis.com/datasets/3e0c1eb04e5c48b3be9040b0589d3ccf_8.csv'
state_boundaries = 'http://data-lakecountyil.opendata.arcgis.com/datasets/3e0c1eb04e5c48b3be9040b0589d3ccf_8.geojson'

We will use the ‘state_boundaries’ file to visualize the boundaries and areas covered by each state on our folium.Map object. This is an overlay on our original map and similarly, we can visualize multiple layers on the same map. This overlay will assist us in creating our choropleth map that is discussed ahead.

folium.GeoJson(state_boundaries).add_to(usa_map)
usa_map

The ‘obesity_data’ dataframe can be viewed below. It contains 5 variables. However, for the purpose of this demonstration, we are only concerned with the ‘NAME’ and ‘Obesity’ attributes.

obesity_data = pd.read_csv(obesity_link)
obesity_data.head()

Choropleth map

Now comes the most interesting part! Creating a choropleth map. We’ll bind the ‘obesity_data’ data frame with our ‘state_boundaries’ geojson file. We have assigned both the data files to our variables ‘data’ and ‘geo_data’ respectively. The columns parameter indicates which DataFrame columns to use, whereas, the key_on parameter indicates the layer in the GeoJSON on which to key the data.

We have additionally specified several other parameters that will define the color scheme we’re going to use. Colors are generated from Color Brewer’s sequential palettes.

By default, linear binning is used between the min and the max of the values. Custom binning can be achieved with the bins parameter.

folium. Choropleth( geo_data = state_boundaries,
    name = 'choropleth',
    data = obesity_data,
    columns = ['NAME', 'Obesity'],
    key_on = 'feature.properties.NAME',
    fill_color = 'YlOrRd',
    fill_opacity = 0.9,
    line_opacity = 0.5,
    legend_name = 'Obesity Percentage').add_to(usa_map)
folium.LayerControl().add_to(usa_map)
usa_map

You might now feel powerful enough after attaining the skill to visualize spatial data effectively. Go ahead and explore Folium‘s documentation to discover the incredible capabilities that this open-source library has to offer.

Thanks for reading! If you want more datasets to play with, check out this blog post. It consists of 30 free datasets with questions for you to solve.

References:

• Python Visualization Modules
• Python Visualization Quickstart
• Seattle Real time Fire Dataset
• https://catalog.data.gov/dataset/national-obesity-by-state
• National Obesity Dataset

Written by Rahim Rasool