Data Visualization

Research requires various tools to collect, analyze and disseminate information effectively. Some essential research tools include search engines like Google Scholar, JSTOR, and PubMed, reference management software like Zotero, Mendeley, and EndNote, statistical analysis tools like SPSS, R, and Stata, writing tools like Microsoft Word and Grammarly, and data visualization tools like Tableau and Excel.  

1. Google Scholar – Google Scholar is a search engine for scholarly literature, including articles, theses, books, and conference papers.

2. JSTOR – JSTOR is a digital library of academic journals, books, and primary sources.

3.PubMed – PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. 

4. Web of Science: Web of Science is a citation index that allows you to search for articles, conference proceedings, and books across various scientific disciplines. 

5. Scopus – Scopus citation database that covers scientific, technical, medical, and social sciences literature. 

6. Zotero: Zotero is a free, open-source citation management tool that helps you organize your research sources, create bibliographies, and collaborate with others.

7. Mendeley – Mendeley is a reference management software that allows you to organize and share your research papers and collaborate with others.

8. EndNote – EndNoted is a software tool for managing bibliographies, citations, and references on the Windows and macOS operating systems. 

9. RefWorks – RefWorks is a web-based reference management tool that allows you to create and organize a personal database of references and generate citations and bibliographies.

10. Evernote – Evernote is a digital notebook that allows you to capture and organize your research notes, web clippings, and documents.

11. SPSS – SPSS is a statistical software package used for data analysis, data mining, and forecasting.

12. R – R is a free, open-source software environment for statistical computing and graphics.

13. Stata – Stata is a statistical software package that provides a suite of applications for data management and statistical analysis.

Other helpful tools for collaboration and organization include NVivo, Slack, Zoom, and Microsoft Teams. With these tools, researchers can effectively find relevant literature, manage references, analyze data, write research papers, create visual representations of data, and collaborate with peers. 

14. Excel – Excel is spreadsheet software used for organizing, analyzing, and presenting data.

15. Tableau – Tableau is a data visualization software that allows you to create interactive visualizations and dashboards.

16. NVivo – Nviva is a software tool for qualitative research and data analysis.

17. Slack – Slack is a messaging platform for team communication and collaboration.

18. Zoom – Zoom is a video conferencing software that allows you to conduct virtual meetings and webinars.

19. Microsoft Teams – Microsoft Teams is a collaboration platform that allows you to chat, share files, and collaborate with your team.

20. Qualtrics – Qualtrics is an online survey platform that allows researchers to design and distribute surveys, collect and analyze data, and generate reports.

Maximizing accuracy and efficiency with research tools

Research is a vital aspect of any academic discipline, and it is critical to have access to appropriate research tools to facilitate the research process. Researchers require access to various research tools and software to conduct research, analyze data, and report research findings. Some standard research tools researchers use include search engines, reference management software, statistical analysis tools, writing tools, and data visualization tools.

Specialized research tools are also available for researchers in specific fields, such as GIS software for geographers and geneticist gene sequence analysis tools. These tools help researchers organize data, collaborate with peers, and effectively present research findings.

It is crucial for researchers to choose the right tools for their research project, as these tools can significantly impact the accuracy and reliability of research findings.

Conclusion

Summing it up, researchers today have access to an array of essential research tools that can help simplify the research process. From data collection to analysis and presentation, these tools make research more accessible, efficient, and accurate. By leveraging these tools, researchers can improve their work and produce more high-quality research.

Have you ever heard a story told with numbers? That’s the magic of data storytelling, and it’s taking the world by storm. If you’re ready to captivate your audience with compelling data narratives, you’ve come to the right place.

Everyone loves data—it’s the reason your organization is able to make informed decisions on a regular basis. With new tools and technologies becoming available every day, it’s easy for businesses to access the data they need rather than search for it. Unfortunately, this also means that increasingly people are seeing the ins and outs of presenting data in an understandable way.

The rise in social media has allowed people to share their experiences with a product or service without having to look them up first. As a result, businesses are being forced to present data in a more refined way than ever before if they want to retain customers, generate leads, and retain brand loyalty. 

What is data storytelling? 

Data storytelling is the process of using data to communicate the story behind the numbers—and it’s a process that’s becoming more and more relevant as more people learn how to use data to make decisions. In the simplest terms, data storytelling is the process of using numerical data to tell a story. A good data story allows a business to dive deeper into the numbers and delve into the context that led to those numbers.

For example, let’s say you’re running a health and wellness clinic. A patient walks into your clinic, and you diagnose that they have low energy, are stressed out, and have an overall feeling of being unwell. Based on this, you recommend a course of treatment that addresses the symptoms of stress and low energy. This data story could then be used to inform the next steps that you recommend for the patient.   

Why is data storytelling important in three main fields: Finance, healthcare, and education? 

Finance – With online banking and payment systems becoming more common, the demand for data storytelling is greater than ever. Data can be used to improve a customer journey, improve the way your organization interacts with customers, and provide personalized services. Healthcare – With medical information becoming increasingly complex, data storytelling is more important than ever. In education – With more and more schools turning to data to provide personalized education, data storytelling can help drive outcomes for students. 

The importance of authenticity in data storytelling 

Authenticity is key when it comes to data storytelling. The best way to understand the importance of authenticity is to think about two different data stories. Imagine that in one, you present the data in a way that is true to the numbers, but the context is lost in translation. In the other example, you present the data in a more simplified way that reflects the situation, but it also leaves out key details. This is the key difference between data storytelling that is authentic and data storytelling that is not.

As you can imagine, the data store that is not authentic will be much less impactful than the first example. It may help someone, but it likely won’t have the positive impact that the first example did. The key to authenticity is to be true to the facts, but also to be honest with your readers. You want to tell a story that reflects the data, but you also want to tell a story that is true to the context of the data. 

How to do data storytelling in action?

Start by gathering all the relevant data together. This could include figures from products, services, and your business as a whole; it could also include data about how your customers are currently using your product or service. Once you have your data together, you’ll want to begin to create a content outline.

This outline should be broken down into paragraphs and sentences that will help you tell your story more clearly. Invest time into creating an outline that is thorough but also easy for others to follow.

Next, you’ll want to begin to find visual representations of your data. This could be images, infographics, charts, or graphs. The visuals you choose should help you to tell your story more clearly.

Once you’ve finished your visual content, you’ll want to polish off your data stories. The last step in data storytelling is to write your stories and descriptions. This will give you an opportunity to add more detail to your visual content and polish off your message. 

The need for strategizing before you start 

While the process of data storytelling is fairly straightforward, the best way to begin is by strategizing. This is a key step because it will help you to create a content outline that is thorough, complete, and engaging. You’ll also want to strategize by thinking about who you are writing your stories for. This could be a specific section of your audience, or it could be a wider audience. Once you’ve identified your audience, you’ll want to think about what you want to achieve.

This will help you to create a content outline that is targeted and specific. Next, you’ll want to think about what your content outline will look like. This will help you to create a content outline that is detailed and engaging. You’ll also want to consider what your content outline will include. This will help you to ensure that your content outline is complete, and that it includes everything you want to include. 

Planning your content outline 

There are a few key things that you’ll want to include in your content outline. These include audience pain points, a detailed overview of your content, and your strategy. With your strategy, you’ll want to think about how you plan to present your data. This will help you to create a content outline that is focused, and it will also help you to make sure that you stay on track. 

Watch this video to know what your data tells you

Researching your audience and understanding their pain points 

With the planning complete, you’ll want to start to research your audience. This will help you to create a content outline that is more focused and will also help you to understand your audience’s pain points. With pain points in mind, you’ll want to create a content outline that is more detailed, engaging, and honest. You’ll also want to make sure that you’re including everything that you want to include in your content outline.   

Next, you’ll want to start to research your pain points. This will help you to create a content outline that is more detailed and engaging. 

Before you begin to create your content outline, you’ll want to start to think about your audience. This will help you to make connections and to start creating your content outline. With your audience in mind, you’ll want to think about how to present your information. This will help you to create a content outline that is more detailed, engaging, and focused. 

The final step in creating your content outline is to decide where you’re going to publish your data stories. If you’re going to publish your content on a website, you should think about the layout that you want to use. You’ll want to think about the amount of text and the number of images you want to include. 

The need for strategizing before you start 

Just as a good story always has a beginning, a middle, and an end, so does a good data story. The best way to start is by gathering all the relevant data together and creating a content outline. Once you’ve done this, you can begin to strategize and make your content more engaging, and you’ll want to make sure that you stay on track. 

Mastering your message: How to create a winning content outline

The first thing that you’ll want to think about when it comes to planning your content outline is your strategy. This will help you to make sure that you stay on track with your content outline. Next, you’ll want to think about your audience’s pain points. This will help you to make sure that you stay focused on the most important aspects of your content.  

Researching your audience and understanding their pain points 

The final thing that you’ll want to do before you begin to create your content outline is to research your audience. This will help you to make sure that you stay focused on the most important aspects of your content. With pain points in mind, you’ll want to make sure that you stay focused on the most important aspects of your content.  

Next, you’ll want to start to research your audience. This will help you to make sure that you stay focused on the most important aspects of your content. 

By approaching data storytelling in this way, you should be able to create engaging, detailed, and targeted content. 

The bottom line: What we’ve learned

Are you geared to create a sales dashboard on Power BI and track key performance indicators to drive sales success? This step-by-step guide will show you through connecting to the data source, build the dashboard, and add interactivity and filters.

Creating a sales dashboard in Power BI is a straightforward process that can help your sales team to track key performance indicators (KPIs) and make data-driven decisions. Here’s a step-by-step guide on how to create a sales dashboard using the above-mentioned KPIs in Power BI: 

Step 1: Connect to your data source 

The first step is to connect to your data source in Power BI. This can be done by clicking on the “Get Data” button in the Home ribbon, and then selecting the appropriate connection type (e.g., Excel, SQL Server, etc.). Once you have connected to your data source, you can import the data into Power BI for analysis. 

Step 2: Create a new report 

Once you have connected to your data source, you can create a new report by clicking on the “File” menu and selecting “New” -> “Report.” This will open a new report canvas where you can begin to build your dashboard. 

Step 3: Build the dashboard 

To build the dashboard, you will need to add visualizations to the report canvas. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen, and then selecting the appropriate visualization type (e.g., bar chart, line chart, etc.). Once you have added a visualization to the report canvas, you can use the “Fields” pane on the right-hand side to add data to the visualization. 

Read more about maximizing sales success with dashboards by clicking on this link.

Step 4: Add the KPIs to the dashboard 

To add the KPIs to the dashboard, you will need to create a new card visualization for each KPI. Then, use the “Fields” pane on the right-hand side of the screen to add the appropriate data to each card. 

Sales Revenue:

To add this KPI, you’ll need to create a card visualization and add the “Total Sales Revenue” column from your data source. 

Sales Quota Attainment:

To add this KPI, you’ll need to create a card visualization and add the “Sales Quota Attainment” column from your data source. 

Lead Conversion Rate:

To add this KPI, you’ll need to create a card visualization and add the “Lead Conversion Rate” column from your data source. 

Customer Retention Rate:

To add this KPI, you’ll need to create a card visualization and add the “Customer Retention Rate” column from your data source. 

Average Order Value:

To add this KPI, you’ll need to create a card visualization and add the “Average Order Value” column from your data source. 

Step 5: Add filters and interactivity 

Once you have added all the KPIs to the dashboard, you can add filters and interactivity to the visualizations. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen and selecting the appropriate filter or interactivity option. For example, you can add a time filter to your chart to show sales data over a specific period, or you can add a hover interaction to your diagram to show more data when the user moves their mouse over a specific point.

Check out this course and learn Power BI today!

Step 6: Publish and share the dashboard 

Once you’ve completed your dashboard, you can publish it to the web or share it with specific users. To do this, click on the “File” menu and select “Publish” -> “Publish to Web” (or “Share” -> “Share with specific users” if you are sharing the dashboard with specific users). This will generate a link that can be shared with your team, or you can also publish the dashboard to the Power BI service where it can be accessed by your sales team from anywhere, at any time. You can also set up automated refresh schedules so that the dashboard is updated with the latest data from your data source.

Ready to transform your sales strategy with a custom dashboard in Power BI?

By creating a sales dashboard in Power BI, you can bring all your sales data together in one place, making it easier for your team to track key performance indicators and make informed decisions. The process is simple and straightforward, and the end result is a custom dashboard that can be customized to fit the specific needs of your sales team.

Whether you are looking to track sales revenue, sales quota attainment, lead conversion rate, customer retention rate, or average order value, Power BI has you covered. So why wait? Get started today and see how Power BI can help you drive growth and success for your sales team! 

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

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

Volume 

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

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

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

Velocity 

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

Variety 

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

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

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

Variability 

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

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

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

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

Veracity 

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

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

Validity 

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

Volatility

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

Vulnerability 

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

Visualization  

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

Value 

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

Learn about 10 Vs of big data by George Firican

10 Vs of Big Data 

Enable smart decision making with big data visualization

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

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

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

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

Dashboarding has become an increasingly popular tool for sales teams, and good reason. A well-designed dashboard can help sales teams to track key performance indicators (KPIs) in real-time, which can provide valuable insights into sales performance and help teams to make data-driven decisions.

In this blog post, we’ll explore the importance of dashboarding for sales teams, and highlight five KPIs that every sales team should track. 

Sales revenue:

This is the most basic KPI for a sales team, and it simply represents the total amount of money generated from sales. Tracking sales revenue can help teams to identify trends in sales performance and can be used to set and track sales goals. It’s also important to track sales revenue by individual product, category, or sales rep to understand the performance of different areas of the business. 

Sales quota attainment:

Sales quota attainment measures how well a sales team performs against its goals. It is typically expressed as a percentage and is calculated by dividing the total sales by the sales quota. Tracking this KPI can help sales teams to understand how they are performing against their goals and can identify areas that need improvement. 

Read more about: Data science to boost eCommerce sakes

Lead conversion rate:

The lead conversion rate is a measure of how effectively a sales team is converting leads into paying customers. It is calculated by dividing the number of leads that are converted into sales by the total number of leads generated. Tracking this KPI can help sales teams to understand how well their lead generation efforts are working and can identify areas where improvements can be made. 

Customer retention rate:

The customer retention rate is a measure of how well a company is retaining its customers over time. It is calculated by dividing the number of customers at the end of a given period by the number of customers at the beginning of that period, multiplied by 100. By tracking customer retention rate over time, sales teams can identify patterns in customer behavior, and use that data to develop strategies for improving retention.  

Average order value:

Average order value (AOV) is a measure of the amount of money a customer spends on each purchase. It is calculated by dividing the total revenue by the total number of orders. AOV can be used to identify trends in customer buying behavior and can help sales teams to identify which products or services are most popular among customers. 

All these KPIs are important for a sales team as they allow them to measure their performance and how they are doing against the set goals.

Sales revenue is important to understand the total money generated from sales, sales quota attainment gives a measure of how well the team is doing against their set targets, lead conversion rate helps understand the effectiveness of lead generation, the customer retention rate is important to understand the patterns of customer behavior and the average order value helps understand which products are most popular among the customers. 

Read about: Big data problem, its impact, and a solution for it

All of these KPIs can provide valuable insights into sales performance and can help sales teams to make data-driven decisions. By tracking these KPIs, sales teams can identify areas that need improvement, and develop strategies for increasing sales, improving lead conversion, and retaining customers.

A dashboard can be a great way to visualize this data, providing an easy-to-use interface for tracking and analyzing KPIs. By integrating these KPIs into a sales dashboard, teams can see a clear picture of performance in real time and make more informed decisions. 

Take data-driven decisions today with creative dashboards!

In conclusion, dashboarding is an essential tool for sales teams as it allows them to track key performance indicators and provides a clear picture of their performance in real time. It can help them identify areas of improvement and make data-driven decisions. Sales revenue, sales quota attainment, lead conversion rate, customer retention rate, 

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

What is Exploratory Data Analysis (EDA)? 

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

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

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

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

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

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

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

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

Importance of EDA: 

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

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

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

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

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

Common EDA techniques: 

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

Visualization:  

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

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

Histogram: 

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

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

Pie Chart: 

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

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

Box Plot: 

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

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

Descriptive statistics:  

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

Grouping and aggregating:  

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

Data cleaning:  

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

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

Correlation analysis: 

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

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

Types of EDA: 

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

Univariate EDA:  

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

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

Bivariate EDA:  

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

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

Multivariate EDA:  

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

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

Time-series EDA:  

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

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

Spatial EDA:  

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

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

Popular libraries for EDA: 

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

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

Conclusion: 

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

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

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

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

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

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

Recommended data visualization tools you must know about  

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

1. Visual.ly 

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

2. Sisense 

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

3. Data wrapper 

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

4. Zoho reports 

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

5. Highcharts 

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

6. Qlikview 

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

7. Sigma.js 

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

8. JupyteR 

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

9. Google charts 

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

10. Fusioncharts 

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

11. Infogram 

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

12. Polymaps 

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

13. Tableau 

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

14. Klipfolio 

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

15. Domo 

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

16. Looker 

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

17. Qlik sense 

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

18. Grafana 

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

19. Chartist.js 

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

20. Chart.js 

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

21. D3.js 

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

22. ChartBlocks 

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

23. Microsoft Power BI 

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

24. Plotly 

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

25. Excel 

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

26. IBM watson analytics 

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

27. FushionCharts 

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

28. Dundas BI 

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

29. RAW 

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

30. Redash 

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

31. Dygraphs 

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

32. RapidMiner 

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

33. Gephi 

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

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

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

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

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

Who uses Power BI? 

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

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

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

 Loading dataset in Power BI 

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

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

Power Query Editor 

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

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

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

Build visuals with your data 

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

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

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

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

Number of visualizations options offered by Power BI 

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

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

Career opportunities with Power BI

Power BI:

Analyst
Software Engineer
Senior Business Intelligence Analyst

Business Analyst
Data Analyst
Developer

Senior Software Engineer

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

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

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

Begin learning Power BI now!

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

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

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

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

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

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

1. Invest in accurate data  

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

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

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

2. Use real-time data to be unique  

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

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

3. Create a story 

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

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

4. Promote visualizations perfectly 

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

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

5. Gather and present unique data  

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

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

6. Know your audience 

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

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

7. Understand your craft 

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

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

8. Learn from the best  

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

 9. Gather the respective data visualization tool 

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

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

10. Research and testing  

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

Need for data visualization

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

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

Required data visualization packages

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

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

Data

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

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

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

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

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

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

How to work with maps

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

Mapping with ggplot2 package

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Maps with ggmap package, Google Maps API and others

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

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

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

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

How to create and plot a map…

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

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

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

How to change the zoom in the map…

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

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

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

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

How to change the type of map…

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

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

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

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

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

How to change the source for maps…

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

# An example using different map services as a source

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

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

How to geocode a location…

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

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

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

How to find a route between two locations…

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

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

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

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

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

How to find the distance between two locations…

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

Pro tip: Learn to use data to drive decision making

More on mapping

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

References

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

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

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

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

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

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

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

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

What is data visualization?

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

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

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

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()

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

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

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

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

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

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

seattle911.head()

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

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

for i in range(len(seattle911)):

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

Voila! The map above gives us insights about where and what 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

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

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

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

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

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

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

Choropleth map

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

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

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

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

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

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

References:

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

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:

Enjoy the video of Power BI!

Learn more about Power BI with Data Science Dojo

Designers don’t need to use data-driven decision-making, right? Here are 5 common design problems you can solve with the data science basics.

What are the common design problems we face every day?

Design is a busy job. You have to balance both artistic and technical skills and meet the needs of bosses and clients who might not know what they want until they ask you to change it. You have to think about the big picture, the story, and the brand, while also being the person who spots when something is misaligned by a hair’s width.

The ‘real’ artists think you sold out, and your parents wish you had just majored in business. When you’re juggling all of this, you might think to yourself, “at least I don’t have to be a numbers person,” and you avoid complicated topics like data analytics at all costs.

If you find yourself thinking along these lines, this article is for you. Here are a few common problems you might encounter as a designer, and how some of the basic approaches of data science can be used to solve them. It might actually take a few things off your plate.

1. The person I’m designing for has no idea what they want

If you have any experience with designing for other people, you know exactly what this really means. You might be asked to make something vague such as “a flyer that says who we are to potential customers and has a lot of photos in it.” A dozen or so drafts later, you have figured out plenty of things they don’t like and are no closer to a final product.

What you need to look for are the company’s needs. Not just the needs they say they have; ask them for the data. The company might already be keeping their own metrics, so ask what numbers most are concerning to them, and what goals they have for improvement. If they say they don’t have any data like that – FALSE!

Every organization has some kind of data, even if you have to be the one to put it together. It might not even be in the most obvious of places like an Excel file. Go through the customer emails, conversations, chats, and your CRM, and make a note of what the most usual questions are, who asks them, and when they get sent in. You just made your own metrics, buddy!

Now that you have the data, gear your design solutions to improve those key metrics. This time when you design the flyer, put the answers to the most frequent questions at the top of the visual hierarchy. Maybe you don’t need a ton of photos but select one great photo that had the highest engagement on their Instagram. No matter how picky a client is, there’s no disagreeing with good data.

2. I have too much content and I don’t know how to organize it

This problem is especially popular in digital design. Whether it’s an app, an email, or an entire website, you have a lot of elements to deal with, and need to figure out how to navigate the audience through all of it. For those of you who are unaware, this is the basic concept of UX, short for ‘User Experience.’

The dangerous trap people fall into is asking for opinions about UX. You can ask 5 people or 500 and you’re always going to end up with the same conclusion: people want to see everything, all at once, but they want it to be simple, easy to navigate and uncrowded.

The perfect UX is basically impossible, which is why you instead need to focus on getting the most important aspects and prioritizing them. While people’s opinions claim to prioritize everything, their actual behavior when searching for what they want is much more telling.

Capturing this behavior is easy with web analytics tools. There are plenty of apps like Google Analytics to track the big picture parts of your website, but for the finer details of a single web page design, there are tools like Hotjar. You can track how each user (with cookies enabled) travels through your site, such as how far they scroll and what elements they click on.

If users keep leaving the page without getting to the checkout, you can find out where they are when they decide to leave, and what calls to action are being overlooked.

When you really get the hang of it, UX will transform from a guessing game about making buttons “obvious” and instead you will understand your site as a series of pathways through hierarchies of story elements. As an added bonus, you can apply this same knowledge to your print media and make uncrowded brochures and advertisements too!

3. I’m losing my mind to a handful of arbitrary choices

Should the dress be pink, or blue? Unfortunately, not all of us can be Disney princesses with magic wands to change constantly back and forth between colors. Unless, of course, you are a web designer from the 90’s, and in that case, those rainbow shifting gifs on your website are wicked gnarly, dude.

For the rest of us, we have to make some tough calls about design elements. Even if you’re used to making these decisions, you might be working with other people who are divided over their own ideas and have no clue who to side with. (Little known fact about designers: we don’t have opinions on absolutely everything.)

This is where a simple concept called “A/B testing” comes in handy. It requires some coding knowledge to pull it off yourself or you can ask your web developer to install the tracking pixel, but some digital marketing tools have built-in A/B testing features. (You can learn more about A/B testing in Data Science Dojo’s comprehensive bootcamps cough cough)

Other than the technical aspect, it’s beautifully simple. You take a single design element, and narrow it down to two options, with a shared ultimate goal you want that element to contribute to. Half your audience will see the pink dress, and half will see the blue, and the data will show you not only which dress was liked by the princesses, but exactly how much more they liked it. Just like magic.

4. I’m working with someone who is using Comic Sans, Papyrus, or (insert taboo here) unironically

This is such a common problem, so well understood that the inside jokes about it between designer’s risk flipping all the way around the scale into a genuine appreciation of bad design elements. But what do you do when you have a person who sincerely asks you what’s wrong with using the same font Avatar used in their logo?

The solution to this is kind of dirty and cheap from the data science perspective, but I’m including it because it follows the basic principle of evidence > intuition. There is no way to really explain a design faux-pas because it comes from experience. However, sometimes when experience can’t be described, it can be quantified.

Ask this person to look up the top competitors in their sector. Then ask them to find similar businesses using this design element you’re concerned about. How do these organizations compare? How many followers do they have on social media? When was the last time they updated something? How many reviews do they have?

If the results genuinely show that Papyrus is the secret ingredient to a successful brand, then wow, time to rethink that style guide.

5. How can I prove that my designs are “good”?

Unless you have skipped to the end of this article, you already know the solution to this one. No matter what kind of design you do, it’s meant to fulfill a goal. And where do data scientists get goals? Metrics! Some good metrics for UX that you might want to consider when designing a website, email, or ad campaign are click-through-rate (CTR), session time, page views, page load, bounce rate, conversions, and return visits.

This article has already covered a few basic strategies to get design related metrics. Even if the person you’re working for doesn’t have the issues described above (or maybe you’re working for yourself) it’s a great idea to look at metrics before and after your design hits the presses.

If the data doesn’t shift how, you want it to, that’s a learning experience. You might even do some more digging to find data that can tell you where the problem came from, if it was a detail in your design or a flaw in getting it delivered to the audience.

When you do see positive trends, congrats! You helped further your organization’s goals and validated your design skills. Attaching tangible metrics to your work is a great support to getting more jobs and pay raises, so you don’t have to eat ramen noodles forever.

If nothing else, it’s a great way to prove that you didn’t need to major in accounting to work with fancy numbers, dad.

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.

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. 

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. 

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.