Visualizing your love for data

This post is in celebration of the love data week between Feb-13-Feb 17, 2023. 

Analytics screen graph.
Photo by Luke Chesser on Unsplash 

What is Data visualization?  

For this author, it was love at first sight. Well, technically, it was love at first Visualization. So many say seeing is believing, and data visualization helps us accomplish that, especially at the rate at which data is increasing exponentially in our world. The truth is that data is everywhere, and for us to draw meaning from it, we need to present it in a clear and concise manner.  

Data visualization is the graphical representation of data. Data can be represented in various forms and shapes, such as maps, charts, infographics, graphs, heat maps, or sparklines. When data is presented through visual elements, it is easy to understand and analyze. It helps you to derive meaning from the data and make better decisions. Visualizing your data involves using certain tools; these tools help you fall more in love with data.  

Data Visualization tools are software that allow you to create graphical representations of your data.  

Here are some tools to help you get started. These have been selected based on their ease of use, features (such as capacity for large volumes of data), cost, and popularity.

  1. Data Wrapper: If you are just starting out with data Visualization and you are looking for a free tool to help you get started, Data wrapper is your plug. Data Wrapper is a beginner-friendly tool with a clean and intuitive user interface accessible online. It is straightforward to navigate and great for creating charts and maps that can be easily embedded into reports. It also allows you to upload your files in various formats such as CSV, .tsv, and .txt 

Pros: 

  • Great for beginners.
  • Free to use.
  • Accessible online tool.

Cons:  

  • It can be challenging to build complex charts. 
  • Limited features. 
  • Security is not guaranteed as it is an online tool.
  1. Infogram: If you are not super design-inclined, this visualization tool should be your best friend. It has an editor drag-and-drop feature that makes it super easy to create beautiful designs without having to worry about where you are with your design skills. Infographics, marketing reports, maps, social media posts, and many more are examples of what you can create with this powerful tool. In addition, your data output can be exported in various formats, such as. JPG, GIF, PNG, HTML, and . PDF.  

Pros:

  • Web-based. 
  • Drag-and-drop editor.
  • Easy to use.
  • Highly customizable.

Cons: 

  • Built-in data sources are limited.
  • Not suitable for complex visualization.
  1. Google charts: Google Charts is another free data visualization tool that is user-friendly and compatible with all browsers and platforms. If you like to play around with codes, then Google Charts provides you with that option. Google Charts are coded with SVG and HTML5, allowing it to produce several graphic and pictorial data visualizations, ranging from simple visualization such as pie charts, bars, charts, histograms, maps, and scatter graphs to more complex ones such as hierarchical tree maps, timelines, and gauges. Google fusion tables, spreadsheets, and SQL databases are examples of data sources that can be used with Google Charts.  

Pros:

  • It is free.
  • It is compatible with various browsers.
  • Compatible with google products.

Cons:

  • Technical support is limited.
  • It requires network connectivity for visualization. 
  • There is no room for customization. 
  1. Tableau: This is one of the most popular data visualization tools, mainly because of the free public version that this software provides. Tableau provides the option of a desktop app, server, and online versions. In addition, this software has several data importation options, such as CSV files for google ads. Similarly, if you are looking into presenting your data in various formats, such as multiple chart formats and mapping, then Tableau is the one for you.  

Pros:

  • Provides several options for data import. 
  • It is available for free (public version).

Cons:

  • Lack of Privacy in the public version. 
  • Paid versions are costly. 

5. Dundas BI: Although this is one of the oldest data visualization tools, it is still standing strong as one of the most powerful tools for visualizing data with interactive charts, tree maps, gauges, smart tables, and scorecards. This interactivity allows users to understand the data quickly. Dundas BI is also highly customizable. Dundas BI operates on the ground of responsive HTML5 web technology that allows users to connect, analyze and interact with their data on any device. This powerful tool also provides a built-in feature for extracting data from many data sources.  

Pros:

  • Highly flexible.
  • Provides a variety of visualization options.

Cons: 

  • It lacks predictive analysis. 
  • Does not support 3D charts.  

There you have it! Now you know the tools to ask out on a date when you are ready to visualize your data. As much as you love data, these tools can help make others fall in love with your data, too.   

Of Maps and Memes: A Bit of Cartographic Fun

Co-Authored by Zhaneille Green

We use maps to communicate all the time. Historically, they have been used to navigate the world and to stand as visual, physical manifestations of defined spaces and places. What do you think of when we say “map”: a topographic map1 a transportation map2 or a city map3?

You can use maps to represent just about anything you want to say, far beyond these typical examples. We wrote this blog to invite you to have a little cartographic fun of your own.

If you’re on any kind of social media, you’ve probably seen maps like the one below, highlighting anything from each state’s favorite kind of candy to what the continental US would look like if all of the states’ borders were drawn along rivers and mountain ranges. People definitely seem to enjoy sharing these maps, curious to see what grocery store most people shop at in their home state, or laughing about California’s lack of popularity with the states in the surrounding area.

Map of most popular halloween candy in each US state. View the interactive version on candystore.com

Try your hand at creating your own silly map by using our programs in the Scholarly Commons. Start a war by creating a map that ranks the Southern states with the best barbecue using Adobe Photoshop or Illustrator, or explore a personal hobby like creating a map of all the creatures Sam & Dean Winchester met through the 15 seasons of Supernatural using ArcGIS.

If you’re feeling a bit more serious, don’t fret! Even if these meme-like maps aren’t portraying the most critical information, they do demonstrate how maps can be a great tool for data visualization. In many ways, location can make data feel more personal, because we all have personal connections to place. Admit it: the first thing you checked on the favorite candy map was your home state. Maps also tend to be more visually engaging than a simple table with, for example, states in one column and favorite animal in the other.

Using geotagging data, each dot represents where a photo was taken: blue for locals, red for tourists, and yellow for unknown. Locals and Tourists #1 (GTWA #2): London. Erica Fischer, CC BY-SA 2.0 via Flickr.

Regardless of what you want to map, the Scholarly Commons has the tools to help bring your vision to life. Learn about software access on our website, and check out these LinkedIn Learning resources for an introduction to ArcGIS Online or Photoshop, which are available with University of Illinois login credentials. If you need more assistance, feel free to ask us questions. Go forth and meme!

You can’t analyze data if you ain’t cute: Data Visualization

Meme from Reno 911 with the original text stating "You can't fight crime if you ain't cute" but the "fight crime" is crossed out and above is written "analyze Data"

Humans are highly visual creatures, even more so in our hyper-graphic world of ultra-filtered images and short aesthetic videos. Great ideas are ignored into oblivion in favor of shiny graphics and slick illustrations, so even data analysts need to be aware of how they present their findings. A well-designed infographic will be much more impactful, widely shared, and remembered than columns and rows of numbers. Even a simple graph can help people better come to conclusions and absorb information than they ever would with just numbers alone. People who can not only crunch numbers but also create stunning communications about those numbers are a real asset on the job market, so it behooves any hopeful data analyst to at least learn the basics of visualization.

LinkedIn Learning 

  1. Learning Data Visualization 
    1. This course clocks in at just under two hours and aims to give learners the scaffolding for a strong understanding of data visualization. Geared towards true beginners, this course challenges learners to think about their data, audience, and goals to create visuals that maximize impact. Learners will also learn about visual perception and chart selection strategies, which in turn can set users up for a deep understanding of visualization. 
  1. Data Visualization: Best Practices 
    1. A poorly designed visualization can be criminally misleading, causing viewers to come to biased and inaccurate conclusions that can negatively affect everything from their investment choices to their health practices. This 98-minute course will give learners the tools to avoid common visualization missteps and the tricks to make their visualizations better fit their data, audience, and goals. This course uses Adobe Illustrator, so those who are unfamiliar with the program should first check out this quick start introduction to the program on LinkedIn Learning. Remember, UIUC students have free access many Adobe products, including Adobe Illustrator!  
  2. Excel Data Visualization: Mastering 20+ Charts and Graphs 
    1. Once again, we will focus on this data skillset within the context of a familiar software, Excel. While it is not the first software that comes to mind when thinking about visualization, Excel has surprisingly powerful visualization functions that will certainly come in handy when analyzing data. This course covers the humble pie chart to the complex geospatial heat maps and 3D power maps. In just two hours, learners will be able to quickly take their data from tables to graphics.  

O’Reilly Books and Videos

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  1. Fundamentals of Data Visualization 
    1. This handy book goes deep into the technical aspects of data visualizations. Learners will learn basic concepts like color theory along side more complex practices like redundant coding. This eBook also provides a helpful directory of visualizations so users can quickly find visualizations that fit their needs.
  2. The Data Visualization Lifecycle 
    1. This 4-hour course covers the basics of data visualization but looks at the actual process of professional data visualization that the other resources on this list do not address. Learners will gain technical skills in building visualization and a broader understanding of data visualization as a collaborative process based on external and internal stakeholders and audiences. This course teaches users how to interact with different data cultures, collaborate with colleagues, and how to treat visualization as a product.
  3. Interactive Data Visualization for the Web 
    1. Interactive data visualization is a trending skill in almost all fields that rely on data analysis and visualization of any kind. Allowing others to interact with your data and its visualization can make the data more accessible and memorable than ever before. This book gives users the skills to make interactive visuals with the fundamental concepts and methods of D3, the most powerful JavaScript library for expressing data visually in a web browser. Even those who are new to web programming will learn the basics of HTML, CSS, JavaScript, and SVG alongside the data visualization skills.

In the Catalog 

  1. #MakeoverMonday : improving how we visualize and analyze data, one chart at a time by Andrew Michael Kriebel and Eva Katharina Murray 
    1. Hashtags can be the start of beautiful movements, as those in the data analysis field learned as their #MakeoverMonday tag sparked a complete reimagining of how professionals approach data visualization. Readers will learn concepts of data visualization while viewing the real-life results of these concepts as shown by the hashtag-inspired graphics. #MakeoverMonday shows readers the “many ways to walk the line between simple reporting and design artistry to create exactly the visualization the situation requires”.  
  2. The functional art : an introduction to information graphics and visualization by Alberto Cairo 
    1. If there are data visualization celebrities, then Alberto Cairo is an A-lister. Known for his visualization journalism, he is a self-described information designer who has become famous for his gripping visualizations that stand as both formal art and excellent communication of data. This book allows users to learn the ins and outs of design all while strolling through a gallery of amazing visualization examples. This resource leans heavily on the theory of art and design, which makes it stand out from the other resources on this list. Alberto Cairo’s other works, The Truthful Art: data, charts, and maps for communication and How Charts Lie : getting smarter about visual information  are also worthwhile and insightful reads!  
  3. Data visualisation : a handbook for data driven design by Andy Kirk 
    1. Pivoting back to the more practical side of things, this handbook offers clear and useful processes for data driven designing. Readers will learn more about the visualization workflow, formulating briefs, working with data in the context of visualization, representing data accurately, integrating interactivity, and visualization literacy. 

And that’s it, folks!

With these visualization resources, the Winter Break Data Analysis series is ending on a pretty note. Hopefully, you have been able to keep your mind sharp and develop a new skill over the last month, but even if the timing was off, these resources and many more are available to students all year long! Did you enjoy one of these resources or posts? Do you have questions about any of these topics or suggestions for future series? Please tell us about it at sc@library.illinois.edu or on twitter at @ScholCommons. Thank you for joining this series and happy analyzing!  

*hacker voice* “I’m in” – Coding and Software for Data Analysis

While data analysis has existed in one form or another for centuries, its modern concept is highly tied to a digital environment, which means that people who are looking to move into the data science field will undoubtedly need some technology skills. In the data field, the primary coding languages include Python, R, and SQL. Software is a bit more complicated, with numerous different programs and services used depending on the situation, including Power BI, Spark, SAS, Excel, to name a few. While this is overwhelming, remember that it is not important to become an expert in all of the languages and software. Becoming skilled in one language and a few of the software options, depending on your interest or on the in-demand skills on job listings, will give you the transferable skills to quickly pick up the other languages and software as needed. If this still seems to be  an overwhelming prospect, remember that the best way to eat an elephant is one bite at a time. Take your time, break up the task, and focus on one step at a time! 

LinkedIn Learning

  1. Python for Data Science Essential Training Part 1 
    1.  This 6 hour course guides users through an entire data science project that includes web scrapers, data cleaning and reformatting, generate visualizations, preform simple data analysis and create interactive graphs. The project will have users coding in Python with confidence and give learners a foundation in the Plotly library. Once completed, learners will be able to design and run their own data science projects.  
  1. R for Excel Users 
    1. With Excel being a familiar platform for many interested in data, it is an ideal bridge to more technical skills, like coding in the R language. This course is specifically designed for data analytics with its focus on statistical tasks and operations. It will take user’s Excel skills to another level while also laying a solid foundation for their new R skills. Users will be able to switch between Excel and the R Desctools package to complete tasks seamlessly, using the best of each software to calculate descriptive statistics, run bivariate analyses, and more. This course is for people who are truly proficient in Excel but new to R, so if you need to brush up your Excel skills, go back to the first post in this series and go over the Excel resources!   
  1. SQL Essential Training 
    1. SQL is the language of relational databases, so it is of interest to anyone looking to expand their data handling skills. This training is designed to give data wranglers the tools they need to use SQL effectively using the SQLiteStudio Software. Learners will soon be able to create tables, define relationships, manipulate strings, use triggers to automate actions, and use sub selects and views. Real world examples are used throughout the course and learners will finish the course by building their own SQL application. If you want a gentler introduction to SQL, check out our earlier post on SQL Murder Mystery  

O’Reilly Books and Videos (Make sure to follow these instructions for logging in!) 

  1. Data Analysts Toolbox – Excel, Python, Power BI, Alteryx, Qlik Sense, R, Tableau 
    1. This 46 hour course is not for the faint of heart, but by the end, users will be a Swiss army knife data analyst. This isn’t for true beginners, but rather people who are already familiar with the basic data analysis concepts and have a good grasp of Excel. It is included in this list because it is a great source for learning the basics of the myriad of software and programming languages that data analysts are expected to know, all in one place. The course starts with teaching users about advanced pivot tables, so if users have already mastered the basic pivot table, they should be ready for this course.  
  1. Programming for Data Science: Beginner to Intermediate 
    1. This is an expert curated playlist of courses and book chapters that is designed to help people who are familiar with the math side of data analysis, but not the computer science side. This playlist gives users an introduction to NumPy, Pandas, Python, Spark and other technical data skills. Some previous experience with coding may be helpful in this course, but patience will make up for lack of experience.  

In the Catalog

  1. Python crash course : a hands-on, project-based introduction to programming 
    1. Python is often lauded as one of the most approachable coding languages to learn and its functionality makes it popular in the data science field. So it is no surprise that there are a lot of resources on and off campus for learning Python. This approachable guide is just one of the many resources available to UIUC students, but it stands out with its contents and overall outcomes. “Python Crash Course” covers general programming concepts, Python fundamentals, and problem solving. Unlike some other resources, this guide focuses on many of Python’s uses, not just its data analytics capabilities, which can be appealing to people who want to be more versatile with their skills. However, it is the three projects that make this resource stand out from the rest. Readers will be guided in how to create a simple video game, use data visualization techniques to make graphs and charts, and build an interactive web application.  
  1. The Book of R : a first course in programming and statistics 
    1. R is the most popular coding language for statistical analysis, so it’s clearly important for data analysts to learn. The Book of R is a comprehensive and beginner friendly guide designed for readers who have no previous programming experience or a shaky mathematical foundation as readers will learn both concurrently through the book’s lessons. Starting with writing simple programs and data handling skills, learners will then move forward to producing statistical summaries of data, preforming statistical tests and modeling, create visualizations with contributed packages like ggplot2 and ggvis, write data frames, create functions, and use variables, statements, and loops; statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R; how to access R’s thousands of functions, libraries, and data sets; how to draw valid and useful conclusions from your data; and how to create publication-quality graphics of your results.  

Join us next week for our final installment of the Winter Break Data Analysis series: “You can’t analyze data if you ain’t cute: Data Visualization for Data Analysis”    

Learn Data Analysis: What’s Math Got to do With It?

What’s math got to do with data analysis? Unfortunately, for those of us who are chronic humanities people, math has a lot to do with it. This might seem like a daunting barrier, especially if the last time you looked at a math problem was in a high school algebra class. This is also true for learners who are already skilled with the technological aspect of data analysis but are not familiar with the mathematics side of thing. However, there are so many resources available to help self-directed students learn the basics and get up to speed for the purposes of data analytics! Using the resource platforms described in last week’s blog post, these resources will have even chronic humanities people playing with numbers in no time!  

LinkedIn Learning 

  • Learning Everyday Math 
    • Look, some of us did not absorb or retain the basic math lessons of our early education. That’s okay! This is a no-judgment zone, and this 2 hour course will help users learn how to calculate percentages for tips and taxes, compare prices while shopping, find the area and volume for home-improvement projects, and learn the basics of probability.  
  • Become a Data Scientist 
    • This 21 hour Learning Path is made up of 12 courses that focus more on the statistical side of data analysis than the technical steps of the process. This course is more geared toward users with experience in IT and computers, so it is not the best for people who do not have a strong technical background. However, for those who are familiar with computer science and want to pivot into data analytics, this is an ideal curriculum.   

O’Reilly Books and Videos (Make sure to follow these instructions for logging in!)

  • Essential Math for Data Science 
    • This eBook mixes basic coding skills with math lessons to cover the essential analytical skills needed for data science work. Relevant aspects of calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks are covered in plain English. The chapters include exercises with answers for self-assessment as well as career advice for budding data analysts.   
  • Statistics for Data Science using Python 
    • Besides books, O’Reilly also has expert curated playlists that consist of chapters of several different books, videos and more. This is a great way of getting the most out of several resources to focus on a single skill. This playlist covers the essential statistic concepts found in 11 different resources. Learn about Normal distribution, hypothesis tests, p-values, central limit theorem and more without having to dig for the resources yourself!  
  •   Data Science 101: Methodology, Python, and Essential Math 
    • On top of books and playlists, O’Reilly also has video-based courses. This course covers a lot of data analytics basics, but those who want to focus on the math aspect will benefit from Chapters 15-19. These chapters cover linear algebra, mathematical structures, probability, random variables and multiple variables, and statistical inference.  

In the Catalog 

Be sure to come back next week for the thrilling continuation with “*hacker voice* I’m In: Coding and Software for Data Analysis!