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Transform your data into insights: The data analyst’s guide to Power BI
Ruhma Khawaja
| February 9, 2023

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. 

 

Data Analyst using Power BI
Uses of Power BI for a Data Analyst – Data Science Dojo

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. 

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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.  

 

Mastering the 10 Vs of big data 
Hudaiba Soomro
| January 31, 2023

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. 

10 vs of big data
10 vs of big data

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. 

Mastering Exploratory Data Analysis (EDA): A comprehensive guide
Shehryar Mallick
| January 21, 2023

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. 

Data- Histogram

Histogram
Histogram

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. 

Pie chart- Data

Pie chart
Pie Chart

 

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.  

box plot - data

Box plot
Box Plot

 

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.

data frame - descriptive statistics

descriptive statistics
Descriptive statistics

 

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. 

groupby - data

grouping and aggregation of data
Grouping and Aggregation of 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.  

data cleaning - data frame Data Cleaning

 

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. 

correlation analysis - data frame

correlation analysis
Correlation Analysis

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. 

Pie 2 - data frame

Alcoholism - pie chart
Alcoholism – Pie Chart

 

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

data frame alcoholism

alcoholism data
Alcoholism data

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.
bivariate data frame

Bivariate data chart
Bivariate data chart

 

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.

Multivariate data frame

Multivariate data chart
Multivariate data chart

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. 

time series data frame

Time series data chart
Time Series Data Chart

 

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.

Spatial data frame

Spatial data chart
Spatial data chart

 

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. 

 

33 ways to stunning data visualization 
Hudaiba Soomro
| December 22, 2022

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  

Data visualization - 33 ways

 

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.  

 

  

 

Healthcare data exploration and data visualization using tableau  
Ayesha Saleem
| December 5, 2022

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

(more…)

Educational data exploration and data visualization using Power BI
Ebad Ullah Khan
| November 28, 2022

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

(more…)

Learning Power BI – Crash course
Ayesha Saleem
| November 9, 2022

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. 

Learn Power BI
4 key steps of learning Power BI – Data Science Dojo

 

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

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The seven ingredients in every great chart- A quick webinar recap 
Fatima Rafique
| November 3, 2022

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…)

51 impactful data science quotes by thought leaders 
Ayesha Saleem
| September 7, 2022

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 data science quotes 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 science quotes

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.” 

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. 

 

 

Guest blog
| September 6, 2022

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!  

Data visualization tips

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. 

Data visualization tools are used to gain meaningful insights from data. Learn how to build visualization tools with examples.

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

 

republican map- visualization

 

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")

 

plot map - data visualization

 

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)

 

Google map - data

 

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)

 

google map data visualization

 

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)

 

Google map types

 

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)

 

Google map - Stamen map

 

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).

finding distance between 2 locations

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.

Ali Mohsin
| July 7, 2022

Data Science Dojo has launched Jupyter Hub for Data Visualization using Python offering to the Azure Marketplace with pre-installed data visualization libraries and pre-cloned GitHub repositories of famous books, courses, and workshops which enable the learner to run the example codes provided.

What is data visualization?

It is a technique that is utilized in all areas of science and research. We need a mechanism to visualize the data so we can analyze it because the business sector now collects so much information through data analysis. By providing it with a visual context through maps or graphs, it helps us understand what the information means. As a result, it is simpler to see trends, patterns, and outliers within huge data sets because the data is easier for the human mind to understand and pull insights from the data.

Data visualization using Python

It may assist by conveying data in the most effective manner, regardless of the industry or profession you have chosen. It is one of the crucial processes in the business intelligence process, takes the raw data, models it, and then presents the data so that conclusions may be drawn. Data scientists are developing machine learning algorithms in advanced analytics to better combine crucial data into representations that are simpler to comprehend and interpret.

Given its simplicity and ease of use, Python has grown to be one of the most popular languages in the field of data science over the years. Python has several excellent visualization packages with a wide range of functionality for you whether you want to make interactive or fully customized plots.

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

Data visualization using Python
Using Python to visualize Data

Challenges for individuals

Individuals who want to visualize their data and want to start visualizing data using some programming language usually lack the resources to gain hands-on experience with it. A beginner in visualization with programming language also faces compatibility issues while installing libraries.

What we provide

Our Offer, Jupyter Hub for Visualization using Python solves all the challenges by providing you with an effortless coding environment in the cloud with pre-installed Data Visualization python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Additionally, our offer gives the user access to repositories of well-known books, courses, and workshops on data visualization that include useful notebooks which is a helpful resource for the users to get practical experience with data visualization using Python. The heavy computations required for applications to visualize data are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.   

Listed below are the pre-installed data visualization using python libraries and the sources of repositories of a book to visualize data, a course, and a workshop provided by this offer:

Python libraries:

  • NumPy
  • Matplotlib
  • Pandas
  • Seaborn
  • Plotly
  • Bokeh
  • Plotnine
  • Pygal
  • Ggplot
  • Missingno
  • Leather
  • Holoviews
  • Chartify
  • Cufflinks

Repositories:

  • GitHub repository of the book Interactive Data Visualization with Python, by author Sharath Chandra Guntuku, AbhaBelorkar, Shubhangi Hora, Anshu Kumar.
  • GitHub repository of Data Visualization Recipes in Python, by Theodore Petrou.
  • GitHub repository of Python data visualization workshop, by Stefanie Molin (Author of “Hands-On Data Analysis with Pandas”).
  • GitHub repository Data Visualization using Matplotlib, by Udacity.

Conclusion:

Because the human brain is not designed to process such a large amount of unstructured, raw data and turn it into something usable and understandable form, we require techniques to visualize data. We need graphs and charts to communicate data findings so that we can identify patterns and trends to gain insight and make better decisions faster. Jupyter Hub for Data Visualization using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through our offer, a user can explore various application domains of data visualizations without worrying about the configuration and computations.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Jupyter Notebook Environment dedicated specifically for Data Visualization using Python. The offering leverages the power of Microsoft Azure services to run effortlessly with outstanding responsiveness. Make your complex data understandable and insightful with us and Install the Jupyter Hub offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science!

Try Now!

Muhammad Fahad Alam
| July 8, 2022

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.

Introduction to prognosis prediction

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

Prognosis Prediction Process
Prognosis Prediction Process

Import required modules

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()
data frame
Head Data frame

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()
Describe data frame
Describe data frame
train.describe(include=['object'])
data frame objects
Describe data frame objects

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()
Prognosis Column
Value Count of Prognosis Column

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")
bar chart
Horizontal bar chart for Occurrence column

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']))
Classification report
Classification report

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:

Input Symptoms | Data Science Dojo

Predicted prognoses

Suppose we have these symptoms abdominal pain, acidity, anxiety, and fatigue. To predict prognosis, we must enter the symptoms in comma separate fashion. The system will separate the symptoms, transform them into a form model that can predict and finally output the prognosis.
Output prognosis
Output prognosis

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.

Full Code Available!

Rahim Rasool
| May 22, 2019

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 in 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 with 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
Geospatial visualization of Seattle map
Seattle map centered to the longitude and latitude points in the location parameters.

We can observe the map rendered above. Let’s create another map object with a different tile and zoom_level. Through ‘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 to 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
Folium Seattle map
Folium marker inserted into Seattle map

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 pandas read_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()
Imported dataset of Seattle
Seattle dataset for visualization with longitude and latitude

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()

geospatial visualization python tutorial | Data Science Dojo

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

Advanced features provided by folium

Let us now move towards 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
USA map
Location parameters with US on the 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
USA map
USA map with state boundaries

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()

Obesity data frame (Geospatial analysis)

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

Choropleth map using folium function

Awesome! We’ve been able to create a choropleth map using a simple set of functions offered by Folium. We can visualize the obesity pattern geographically and uncover patterns not visible before. It also helped us in gaining clarity about the data, more than just simplifying the data itself.

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:

Dave Langer
| April 25, 2017

Power BI and R can be used together to achieve analyses that are difficult or impossible to achieve.

It is a powerful technology for quickly creating rich visualizations. It has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis.

Microsoft has also extended Power BI with support for incorporating R visualizations into its projects, enabling a myriad of data visualization use cases across all industries and circumstances. As such, it is an extremely valuable tool for any Data Analyst, Product/Program Manager, or Data Scientist to have in their tool belt.

At the meetup for this topic presenter David Langer showed how it can be using R visualizations to achieve analyses that are difficult, or not possible, to achieve with out-of-the-box features.

A primary focus of the talk was a number of “gotchas” to be aware of when using R Visualizations within the projects:

  • It limits data passed to R visualizations to 150,000 rows.
  • It automatically removes duplicate rows before passing data to it.
  • It allows for permissive column names that can cause difficulties in R code.

David also covered best practices for using R visualizations within its projects, including using R tools like RStudio or Visual Studio R Tools to make R visualization development faster. A particularly interesting aspect of the talk was how to engineer R code to allow for copy-and-paste from RStudio into Power BI.

The talk concluded with examples of how R visualizations can be incorporated into a project to allow for robust, statistically valid analyses of aggregated business data. The following visualization is an example from the talk:

Power BI Process Behavior graph
Power BI Process Behavior

Enjoy the video of Power BI!

Learn more about Power BI with Data Science Dojo

Arsalan Shahid
| March 6, 2019

R and Python remain the most popular data science programming languages. But if we compare r vs python, which of these languages is better?

As data science becomes more and more applicable across every industry sector, you might wonder which programming language is best for implementing your models and analysis. If you attend a data science Bootcamp, Meetup, or conference, chances are you’ll run into people who use one of these languages.

Since R and Python remain the most popular languages for data science, according to  IEEE Spectrum’s latest rankings, it seems reasonable to debate which one is better. Although it’s suggested to use the language you are most comfortable with and one that suits the needs of your organization, for this article, we will evaluate the two languages. We will compare R and Python in four key categories: Data Visualization, Modelling Libraries, Ease of Learning, and Community Support.

Data visualization

A significant part of data science is communication. Most of the time, you as a data scientist need to show your result to colleagues with little or no background in mathematics or statistics. So being able to illustrate your results in an impactful and intelligible manner is very important. Any language or software package for data science should have good data visualization tools.

Good data visualization involves clarity. No matter how complicated your model is, there will be a simple and unambiguous way of illustrating your results such that even a layperson would understand.

Python

Python is renowned for its extensive number of libraries. There are plenty of libraries that can be used for plotting and visualizations. The most popular libraries are matplotlib and  seaborn. The library matplotlib is adapted from MATLAB, it has similar features and styles. The library is a very powerful visualization tool with all kinds of functionality built in. It can be used to make simple plots very easily, especially as it works well with other Python data science libraries, pandas and numpy.

Although matplotlib can make a whole host of graphs and plots, what it lacks is simplicity. The most troublesome aspect is adjusting the size of the plot: if you have a lot of variables it can get hectic trying to neatly fit them all into one plot. Another big problem is creating subplots; again, adjusting them all in one figure can get complicated.

Now, seaborn builds on top of matplotlib, including more aesthetic graphs and plots. The library is surely an improvement on matplotlib’s archaic style, but it still has the same fundamental problem: creating figures can be very complicated. However, recent developments have tried to make things simpler.

R

Many libraries could be used for data visualization in R but ggplot2 is the clear winner in terms of usage and popularity? The library uses a grammar of graphics philosophy, with layers used to draw objects on plots. Layers are often interconnected to each other and can share many common features. These layers allow one to create very sophisticated plots with very few lines of code. The library allows the plotting of summary functions. Thus, ggplot2 is more elegant than matplotlib and thus I feel that in this department R has an edge.

It is, however, worth noting that Python includes a ggplot library, based on similar functionality as the original ggplot2 in R. It is for this reason that R and Python both are on par with each other in this department.

Modelling libraries

Data science requires the use of many algorithms. These sophisticated mathematical methods require robust computation. It is rarely or maybe never the case that you as a data scientist need to code the whole algorithm on your own. Since that is incredibly inefficient and sometimes very hard to do so, data scientists need languages with built-in modelling support. One of the biggest reasons why Python and R get so much traction in the data science space is because of the models you can easily build with them.

Python

As mentioned earlier Python has a very large number of libraries. So naturally, it comes as no surprise that Python has an ample amount of machine learning libraries. There is scikit-learnXGboostTensorFlowKeras and PyTorch just to name a few. Python also has pandas, which allows tabular forms of data. The library pandas make it very easy to manipulate CSVs or Excel-based data.

In addition to this Python has great scientific packages like numpy. Using numpy, you can do complicated mathematical calculations like matrix operations in an instant. All of these packages combined, make Python a powerhouse suited for hardcore modelling.

R

R was developed by statisticians and scientists to perform statistical analysis way before that was such a hot topic. As one would expect from a language made by scientists, one can build a plethora of models using R. Just like Python, R has plenty of libraries — approximately 10000 of them. The mice package, rpartparty and caret are the most widely used. These packages will have your back, starting from the pre-modelling phase to the post-model/optimization phase.

Since you can use these libraries to solve almost any sort of problem; for this discussion let’s just look at what you can’t model. Python is lacking in statistical non-linear regression (beyond simple curve fitting) and mixed-effects models. Some would argue that these are not major barriers or can simply be circumvented. True! But when the competition is stiff you have to be nitpicky to decide which is better. R, on the other hand, lacks the speed that Python provides, which can be useful when you have large amounts of data (big data).

Ease of learning

It’s no secret that currently data science is one of the most in-demand jobs, if not the one most in demand. As a consequence, many people are looking to get on the data science bandwagon, and many of them have little or no programming experience. Learning a new language can be challenging, especially if it is your first. For this reason, it is appropriate to include ease of learning as a metric when comparing the two languages.

Python

Designed in 1989 with a philosophy that emphasizes code readability and a vision to make programming easy or simple, the designers of Python succeeded as the language is fairly easy to learn. Although Python takes inspiration for its syntax from C, unlike C it is uncomplicated. I recommend it as my choice of language for beginners since anyone can pick it up in relatively less time.

R

I wouldn’t say that R is a difficult language to learn. It is quite the contrary, as it is simpler than many languages like C++ or JavaScript. Like Python, much of R’s syntax is based on C, but unlike Python R was not envisioned as a language that anyone could learn and use, as it was specifically initially designed for statisticians and scientists. IDEs such as RStudio have made R significantly more accessible, but in comparison with Python, R is a relatively more difficult language to learn.

In this category Python is the clear winner. However, it must be noted that programming languages in general are not hard to learn. If a beginner wanted to learn R, it won’t be as easy in my opinion as learning Python but it won’t be an impossible task either.

Community support

Every so often as a data scientist you are required to solve problems that you haven’t encountered before. Sometimes you may have difficulty finding the relevant library or package that could help you solve your problem. To find a solution, it is not uncommon for people to search in the language’s official documentation or online community forums. Having good community support can help programmers, in general, to work more efficiently.

Both of these languages have active Stack overflow members and also an active mailing list available (where one can easily ask for solutions from experts). R has online R-documentation where you can find information about certain functions and function inputs. Most Python libraries like pandas and scikit-learn have their official online documentation that explains each library.

Both languages have a significant amount of user base, hence, they both have a very active support community. It isn’t difficult to see that both seem to be equal in this regard.

Why R?

R has been used for statistical computing for over two decades now. You can get started with writing useful code in no time. It has been used extensively by data scientists and has an insane number of packages available for a lot of data science-related tasks. I have almost always been able to find a package in R to get the task done very quickly. I have decent python skills and have written production code in python. Even with that, I find R slightly better for quickly testing out ideas, trying out different ways to visualize data and for rapid prototyping work.

Why Python?

Python has many advantages over R in certain situations. Python is a general-purpose programming language. Python has libraries like pandas, NumPy, scipy and sci-kit-learn, to name a few which can come in handy for doing data science-related work.

If you get to the point where you have to showcase your data science work, Python once would be a clear winner. Python combined with Django is an awesome web application framework, which can help you create a web service/site with both your data science and web programming done in the same language.

You may hear some speed and efficiency arguments from both camps – ignore them for now. If you get to a point when you are doing something substantial enough where the speed of your code matters to you, you will probably figure out things on your own. So don’t worry about it at this point.

You can learn Python for data science with Data Science Dojo!

R and Python – The most popular languages

Considering that you are a beginner in both data science and programming and that you have a background in Economics and Statistics, I would lean towards R. Besides being very powerful, Python is without a doubt one of the friendliest programming languages to beginners – but it is still a programming language. Your learning curve may be a bit steeper in Python as opposed to R.

You should learn Python, once you are comfortable with R, and have grasped the general concepts of data science – which will take some time. You can read “What are the key skills of a data scientist? To get an idea of the skill set you will need to become a data scientist.

Start with R, transition to Python gradually and then start using both as needed. Both are great for data science but one is better than the other in certain situations.

Stephanie Donahole
| May 29, 2019

Data Science is a hot topic in the job market these days. What are some of the best places for Data Scientists and Engineers to work in?

To be honest, there has never been a better time than today to learn data science. The job landscape is quite promising, opportunities span multiple industries, and the nature of the job often allows for remote work flexibility and even self-employment. The following post emphasizes the top cities across the globe with the highest pay packages for data scientists.

Industries across the globe keep diversifying on a constant basis. With technology reaching new heights and a majority of the population having unlimited access to an internet connection, there is no denying the fact that big data and data analytics have started gaining momentum over the years. Demand for data analytics professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions. Further below, I would like to mention certain skills required for a job in data analytics.

Python

Being one of the most used programming languages, Python has a solid understanding of how it can be used for data analytics. Even if it’s not a required skill, knowledge and understanding of Python will give you an upper hand when showing future employers the value that you can bring to their companies. Just make sure you learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications.

SQL (Structured Query Language)

Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way. This will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

Data visualization

Regardless of the career path, you are looking into, it is crucial to visualize and communicate insights related to your company’s services, and is a valuable skill set that will capture the attention of employers. Data scientists are a bit like data translators for other people who exactly know what conclusions to draw from their datasets.

Best opportunities for a data scientist

Have a look at cities across the globe that offer the best opportunities for the position of a data scientist. The order of the cities does not represent any type of rank.

salary graph
Average Salary of a Data Scientist in US Dollars
  1. San Jose, California – Have you ever dreamed about working in Silicon Valley? Who hasn’t? It’s the dream destination of any tech enthusiast and an emerging hot spot for data scientists all across the globe. Being an international headquarters and main offices of the majority of the American tech corporations, it offers a plethora of job opportunities and high pay. It may interest you to know that the average salary of a chief data scientist is estimated to be $132,355 per year.
  2. Bengaluru, India – Second city on the list is Bengaluru, India. The analytics market is touted to be the best in the country, with the state government, analytics startups, and tech giants contributing substantially to the overall development of the sector. The average salary is estimated to be ₹ 12 lakh per annum ($17,240.40).
  3. Berlin, Germany – If we look at other European countries, Germany is home to some of the finest automakers and manufacturers. Although, the country isn’t much explored to newer and better opportunities in the field of data science it seems to be expanding its portfolio day in and day out. If you are a data scientist you may earn around €11,000 but if you are a chief data scientist then you will not be earning less than €114,155.
  4. Geneva, Switzerland – If you are seeking around for one of the highest paying cities in this beautiful paradise; it is Geneva. Call yourself fortunate, if you happen to land a position as a data scientist. The mean salary of a researcher starts at 180,000 Swiss Fr, and a chief data scientist can earn as much as 200,000 Swiss Fr with an average bonus ranging between 9,650-18,000 Swiss Fr.
  5. London, United Kingdom – One of the top destinations in Europe that offers high-paying and reputable jobs in London. UK government seems to rely on technologies day in and day out due to which the number of opportunities in the field has gone up substantially with the average salary of a Data Scientist being £61,543.

I also included the average data scientist salaries from the 20 largest cities around the world in 2019:

  1. Tokyo, Japan: $56,783
  2. New York City, USA: $115,815
  3. Mexico City, Mexico: $32,487
  4. Sao Paolo, Brazil: $45,891
  5. Los Angeles, USA: $120,179
  6. Shanghai, China: $66,014
  7. Mumbai, India: $29,695
  8. Seoul, South Korea: $45,993
  9. Osaka, Japan $54,417
  10. London, UK: $56,820
  11. Lagos, Nigeria: $48,771
  12. Calcutta, India: $7,423
  13. Buenos Aires, Argentina: $40,512
  14. Paris, France: $37,861
  15. Rio de Janeiro, Brazil: $54,191
  16. Karachi, Pakistan: $6,453
  17. Delhi, India: $20,621
  18. Manila, Philippines: $47,414
  19. Istanbul, Turkey: $30,210
  20. Beijing, China: $72,801
Alicia Mitchell
| April 21, 2020

When it comes to using data for social responsibility, one of the most effective ways of dispensing information is through data visualization.

It’s getting harder and harder to ignore big data. Over the past couple of years, we’ve all seen a spike in the way businesses and organizations have ramped up harvesting pertinent information from users and using them to make smarter business decisions. But big data isn’t just for capitalistic purposes — it can also be utilized for social good.

Nathan Piccini discussed in a previous blog post how data scientists could use AI to tackle some of the world’s most pressing issues, including poverty, social and environmental sustainability, and access to healthcare and basic needs. He reiterated how data scientists don’t always have to work with commercial applications and that we all have a social responsibility to put together models that don’t hurt society and its people.

Data visualization and social responsibility

When it comes to using data for social responsibility, one of the most effective ways of dispensing information is through data visualization. The process involves putting together data and presenting it in a form that would be more easily comprehensible for the viewer.

No matter how complex the problem is, visualization converts data and displays it in a more digestible format, as well as laying out not just plain information, but also the patterns that emerge from data sets. Maryville University explains how data visualization has the power to affect and inform business decision-making, leading to positive change.

With regards to the concept of income inequality, data visualization can clearly show the disparities among varying income groups. Sociology professor Mike Savage also reiterated this in the World Social Science Report, where he revealed that social science has a history of being dismissive of the impact of visualizations and preferred textual and numerical formats. Yet time and time again, visualizations proved to be more powerful in telling a story, as it reduces the complexity of data and depicts it graphically in a more concise way.

Take this case study by computational scientist Javier GB, for example. Through tables and charts, he was able to effectively convey how the gap between the rich, the middle class, and the poor has grown over time. In 1984, a time when the economy was booming and the unemployment rate was being reduced, the poorest 50% of the US population had a collective wealth of $600 billion, the middle class had $1.5 trillion, and the top 0.001% owned $358 billion.

Three decades later, the gap has stretched exponentially wider: the poorest 50% of the population had negative wealth that equaled $124 billion, the middle class owned wealth valued $3.3 trillion, while the 0.001% had a combined wealth of $4.8 trillion. By having a graphical representation of income inequality, more people can become aware of class struggles than when they only had access to numerical and text-based data.

The New York Times also showed how powerful data visualization could be in their study of a pool of black boys raised in America and how they earned less than their white peers despite having similar backgrounds. The outlet displayed data in a more interactive manner to keep the reader engaged and retain the information better.

The study followed the lives of boys who grew up in wealthy families, revealing that even though the black boys grew up in well-to-do neighborhoods, they are more likely to remain poor in adulthood than to stay wealthy. Factors like the same income, similar family structures, similar education levels, and similar levels of accumulated wealth don’t seem to matter, either. Black boys were still found to fare worse than white boys in 99 percent of America come adulthood, a stark contrast from previous findings.

Vox also curated different charts collected from various sources to highlight the fact that income inequality is an inescapable problem in the United States. The richest demographic yielded a disproportional amount of economic growth, while wages for the middle class remained stagnant. In one of the charts, it was revealed that in a span of almost four decades, the poorest half of the population has seen its income plummet steadily, while the top 1 percent have only earned more. Painting data in these formats adds more clarity to the issue compared to texts and numbers.

There’s no doubt about it, data visualization’s ability to summarize highly complex information into more comprehensible displays can help with the detection of patterns, trends, and outliers in various data sets. It makes large numbers more relatable, allowing everyone to understand the issue at hand more clearly. And when there’s a better understanding of data, the more people will be inclined to take action.

Sarah John
| February 21, 2022

Instead of loading clients up with bullet points and long-winded analysis, firms should use data visualization tools to illustrate their message.

Every business is always looking for a great way to talk to their customers. Communication between the company’s management team and customers plays an important role. However, the hardest part is finding the best way to communicate with users.

Although it is visible in many companies, many people do not understand the power of visualization in the customer communication industry. This article sheds light on several aspects of how data visualization plays an important role in interacting with clients.

Any interaction between businesses and consumers indicates signs of success between the two parties. Communicating with the customer through visualization is one of the best communication channels that strengthens the relationship between buyers and sellers.

Aspects of data visualization

While data visualization is the best way to communicate, many industry players still don’t understand the power of this aspect. The display helps the commercial teams improve the operating mode of your customer and create an exceptional business environment. Additionally, visualization saves 78% of the time spent capturing customer information to improve services within the enterprise environment.

Data Visualization
Example of Data Visualization

Customer Interactivity

Any business that intends to succeed in the industry needs to have a compelling for customers.

Currently, big data visualization in business has dramatically changed how business talks to clients. The most exciting aspect is that you can use different kinds of visualization.

While using visualization to enhance communication and the entire customer experience, you need to maintain the brand’s image. Also, you can use visualization in marketing your products and services.

To enhance customer interaction, data visualization (Sankey Chart, Radial Bar Chart, Pareto Chart, and Survey Chart, etc.) is used to create dashboards and live sessions that improve the interaction between customers and the business team members. The team members can easily track when customers make changes by using live sessions.

This helps the business management team make the required changes depending on the customer suggestions regarding the business operations. Communication between the two parties continues to create an excellent customer experience by making changes.

Identifying Customers with Repetitive Issues

By creating a good client communication channel, you can easily identify some of the customers who are experiencing problems from time to time. This makes it easier for the technical team to separate customers with recurring issues. 

The technical support team can opt to attach specific codes to the clients with issues to monitor their performance and any other problem. Data visualization helps in separating this kind of data from the rest to enhance clients’ well-being.

It helps when the technical staff communicates with clients individually to identify any problem or if they are experiencing any technical issue. This promotes personalized services and makes customers more comfortable.

Through regular communication between clients and the business management team, the brand gains loyalty making it easier for the business to secure a respectable number of potential customers overall.

Once you have implemented visualization in your business operations, you can solve various problems facing clients using the data you have collected from diverse sources. As the business industry grows, data visualization becomes an integral part in business operations.

This makes the process of solving customer complaints easier and creates a continued communication channel. The data needs to be available in real-time to ensure that the technical support team has everything required to solve any customer problem.

Creating a Mobile Fast Communication Design

The most exciting data visualization application is integrating a dashboard on a website with a mobile fast communication design. This is an exciting innovation that makes it easier for the business to interact with clients from time to time. 

A good number of companies and organizations are slowly catching up with this innovative trend powered by data visualization. A business can easily showcase its stats to its customers on the dashboard to help them understand the milestones attained by the business.

Note that the stats are displayed on the dashboard depending on the customer feedback generated from the business operations. The dashboards have a fast mobile technique that makes communication more convenient.

This aspect is made to help clients access the business website using their mobile phones. An excellent operating mechanism creates a creative and adaptive design that enables mobile phone users to communicate efficiently.

This technique helps showcase information to mobile users, and clients can easily reach out to the business management team and get all their concerns sorted.

Product Performance Analysis

Data visualization is a wonderful way of enhancing the customer experience. Visualization collects data from customers after purchasing products and services to take note of the customer reviews regarding the products and services.

By collecting customer reviews, the business management team can easily evaluate the performance of their products and make the desired changes if the need arises. The data helps reorganize customer behavior and enhance the performance of every product.

The data points recorded from customers are converted into insights vital for the business’s general success. 

Data visuals
Infographic – Data Points into Useful Visuals

Conclusion

Customer communication and experience are major points of consideration for business success. By enhancing customer interaction through charts and other forms of communication, a business makes it easy to flourish and attain its mission in the industry. 

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