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!

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Spaces Highlight: Interview in a Self-Use Media Booth

Media booth interior

Getting an interview is both exciting and nerve-wracking. While I was excited for the opportunity, I knew I would have to deal with the stressors involved with interviewing on Zoom: what to say, what to wear, and where to do the interview. I wanted a place where I could be sure I would not be interrupted, would not have to deal with loud noises, and that would look professional to the interviewers. I decided to take advantage of my workplace’s resources and try out the self-use media studios in Scholarly Commons. I made my appointment on the Scholarly Commons website

The self-use media studios are sound isolation booths with features including two Shure MV7 microphones, Insta360 4k Webcam, LED light banks, three large screens, mac studio, headphones, powered speakers, and Stream Deck. The studios are designed for video recording, podcasting, oral histories, streaming, interviews, video editing, and more. 

I checked into the booth thirty minutes before the start of my interview. The signs posted around the booth told me how to log in, control the audio, and adjust the camera to follow my movements. I experienced a small challenge, when I could not figure out how to get the camera to turn on. But, with the help of Scholarly Commons staff I was able to begin my interview on time and confident in both myself and the technology I was using. 

One of the first things the interviewers asked me was where I was zooming in from. They were extremely impressed with the set up and the professional setting helped me to stand out as a candidate. I felt comfortable speaking at a regular volume, trusting that those outside could not hear what I was saying as I could not hear anything from outside of the booth. The audio was clear on both my side and the interviewers’. 

If you are using the media studios for the first time, you might find these tips helpful: 

  1. Book in advance- the booths are first-come, first-serve and can fill up quickly
  2. Make your booking earlier than your meeting so that you have time to set up and be prepared in case of any challenges
  3. Make sure to read all the signage as they have instructions, helpful tips, and images which help make the booths easier to navigate
  4. If you are having difficulty, ask a staff member as they are happy to help

I found the self-use media studios in Scholarly Commons to be an excellent place to do my interview. If you have an interview coming up or a project that would benefit from the use of an audio booth, I would highly recommend booking one of the media studios. 

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Copyright Enforcement Tools as Censorship

This week, Scholarly Commons graduate assistants Zhaneille Green and Ryan Yoakum, alongside Copyright Librarian Sara Benson, appeared as guest writers for the International Federation of Library Associations and Institutions’ blog as part of a series for Copyright Week. Their blog post looks at how the current copyright tools on platforms such as YouTube and Facebook allow large corporate or governmental entities to silence and suppress individual voices. You can read the full blog post on the IFLA blog website.

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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

Make sure you are logged into O’Reilly before clicking these links. The best way to login is to go to the library catalog’s record for a book offered through O’Reilly (Like this book on Python) and then follow the instructions on this Libguide to log in.

  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!  

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*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”    

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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! 

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Learn Data Analysis Over the Winter Break!

In the last twenty years, humanity has become super proficient in collecting data. Therefore, It is no surprise that the skills to analyze that massive collection of data is in ever increasing demand on the job market. For those of us who are worried about future job prospects, learning these in-demand data analysis skills seems like a logical next step, even if they do not fit into our current degree program. Fortunately, the university has a plethora of self-guided resources available for students looking to build their data skills. What better time to use these resources than during the long winter break?! Over the next few weeks, this blog will delve into the available resources that cover the three main skill areas of data analysis: math, coding and software, and visualization. 

Before diving into those areas, it is wise briefly look at the foundations of data analysis as well as the resources that will be showcased this month. Take this week to get acquainted with these different resource platforms and learn a few starting skills! 

LinkedIn Learning

All UIUC students have access to LinkedIn Learning. Simply login with your NetID credentials, just be sure you are logging into LinkedIn Learning, not the main LinkedIn site.  You will have access to a whole trove of high-quality videos and courses designed to help you learn career-building skills. Not only are the videos professional grade, but they often have accompanying exercise files, learning groups, certificates and exams. The collection ranging from short 5-15 minute videos that teach specific function or skills to dozen hours long courses that are designed to give a comprehensive foundation. The best part of using LinkedIn Learning is that the course and certificates completed here are then displayed on personal LinkedIn pages, showing potential employers that users have the skills they are looking for. 

  • Data Analytics for Students
    • This course is for the true data analytics babies out there. This introduction gives users the basic understanding of what data analytics is, the skills users will need to be successful,  the software and tools common in the field and what careers in data analytics look like. This 1 hour course is well worth the time for those who aren’t sure where to start their data journey.
  • Career Essentials in Data Analysis by Microsoft and LinkedIn
    • Discover the skills needed for a career in data analysis. Learn foundational concepts used in data analysis and practice using software tools for data analytics and data visualization. This is a Learning Path made up of 3 different courses that has about 9 hours of content for students to work through on their own schedule. The courses have exams for self-evaluation as well as a final exam that earns users a professional certificate. 
  • Excel: Managing and Analyzing Data
    • We have all put “proficient in Excel” on a resume, but wouldn’t it be nice if that was actually true? Unlike other data analytics courses, this course focuses on one program that most modern users are already familiar with but do not truly harness the power of. This is ideal for baby data analysts as it doesn’t bombard learners with a whole new software ecosystem but still teaches the transferable skills all data analysts use. Running at just under 4 hours, this course efficiently and comprehensively teaches users impressive data analytics skills. 

O’Reilly Books and Videos

This is a lesser known resource available at UIUC but it has some great online books and videos that tend to focus on the scientific and technical fields. Logging in is not straightforward, unfortunately. The best way to get there is to go to the Library Catalog’s record for a book offered through O’Reilly (Like this book on Python) and then follow the instructions on this LibGuide to log in. Once you are in, you will see a sizable collection of e-books and courses. The materials skew towards the more experienced users, but there are a few resources that will help baby data folks really develop their skills. 

Library Catalog

Learn data science the old fashion way, with books! There are a lot of books available at UIUC libraries for students who want to teach themselves a new skill. Here are a few choices for people looking for an easy introduction to data analysis. The Scholarly Commons collection is easily accessible and found just to the right of the main entrance to the stacks. 

Be sure to check back here next week for our next installment, “What’s Math got to do with it?”!

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Making Infographics in Canva: a Guide and Review

Introduction

If you’ve ever had to design a poster for class, you’re probably familiar with Canva. This online and app-based graphic design tool, with free and subscription-based versions, features a large selection of templates and stock graphics that make it pretty easy to create decent-looking infographics. While it is far from perfect, the ease of use makes Canva worth trying out if you want to add a bit of color and fun to your data presentation.

Getting Started

Starting with a blank document can be intimidating, especially for someone without any graphic design experience. Luckily, Canva has a bunch of templates to help you get started.

Canva infographic templates

I recommend picking a template based on the color scheme and general aesthetic. It’s unlikely you’ll find a template that looks exactly how you want, so you can think of a template as a selection of colors, fonts, and graphics to use in your design, rather than something to just copy and paste things into. For example, see the image below – I recently used the template on the left to create the infographic on the right.

An infographic template compared to the resulting infographic

General Design Principles

Before you get started on your infographic, it’s important to remember some general design guidelines:

  1. Contrast. High levels of contrast between your background and foreground help keep everything legible.
  2. Simplicity. Too many different colors and fonts can be an eyesore. Stick to no more than two fonts at a time.
  3. Space. Leave whitespace to keep things from looking cluttered.
  4. Alignment and balance. People generally enjoy looking at things that are lined up neatly and don’t have too much visual weight on one side or another.
An exaggerated example of a design that ignores the above advice.

Adding Graphs and Graphics

Now that you have a template in hand and graphic design principles in mind, you can start actually creating your infographic. Under “Elements,” Canva includes several types of basic charts. Once you’ve added a chart to your graphic, you can edit the data associated with the chart directly in the provided spreadsheet, by uploading a csv file, or by linking to a google spreadsheet.

Canva interface for creating charts

The settings tab allows you to decide whether you want the chart to include a legend or labels. The options bar at the top allows for further customization of colors and bar or dot appearance. Finally, adding a few simple graphics from Canva’s library such as shapes and icons can make your infographic more interesting. 

Examples of charts available in Canva, with a variety of customizations.

Limitations and Frustrations

The main downsides to Canva are the number of features locked behind a paywall and the inability to see only the free options. Elements cannot be filtered by price and it seems that more and more graphics are being claimed by Canva Pro, so searching for graphics can be frustrating. Templates can be filtered, but it will still bring up results where the template itself is free, but there are paid elements within the template. So, you might choose a template based on a graphic that you really like, only to find out that you need a Canva Pro subscription to include that graphic.

The charts in Canva also have limitations. Pie charts do not allow for the selection of colors for each individual slice; you have to pick one color, and Canva will generate the rest. However, if you want to have more control over your charts, or wish to include more complicated data representations, you can upload charts to Canva, which even supports transparency.

Conclusion

As mentioned above, Canva has its downsides. However, Canva’s templates, graphics, and charts still make it a super useful tool for creating infographics that are visually appealing. Try it out the next time you need to present some data!

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Meet Our Graduate Assistants: Precious Olalere

Precious headshot

What is your educational and/or professional background? 

My undergraduate degree was in Library and Information Science at the University of Ilorin in Nigeria, West Africa. In my prior experience, I worked in a research library where I was able to help researchers and students get access to the right information, I absolutely loved doing this and then I went on to work for Scholars Academy where I gathered some data analytics experience and helped students and researchers with data related questions. 

What led you to your field? 

One key factor that influenced my choice of this field is my ardent love for helping people. Connecting people to the information they are looking for has always been something I enjoyed. Then I realized the philosophies that libraries represent for the people in their communities and how they influence the success of people, which can, in turn, birth a strong nation; all of these are what drove me to the field. 

What are your research interests? 

While I have a broad interest, I am particularly interested in information organization and management, digital libraries, data, and learning analytics. 

What is your specialty within the Scholarly Commons? 

I will be focusing on the data side of things at the Scholarly Commons such as data analysis and data visualization. 

Describe a favorite project you’ve worked on.

This is a hard one because I have enjoyed all the projects I have worked on, particularly the one where I created a small database for a particular library collection. The library had a handwritten manual inventory book used to locate items. To save the amount of time in locating items; I designed a simple inventory database to make access to information faster and easier. 

What Scholarly Commons resource are you most excited to learn about?  

I am really looking forward to learning about room 308 studio booths, I have always loved music and so maybe I will get to record my imaginary music album in it – haha! 

What do you hope to do after graduation? 

While I am still undecided on what I would like to do after graduation, I am really interested in data librarianship and working in the academic sphere.  

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A Non-Data Scientist’s Take on Orange

Introduction

Coming from a background in the humanities, I have recently developed an interest in data analysis but am just learning how to code. While I have been working to remedy that, one of my professors showed me this program known as Orange. Created in 1996, Orange is primarily designed to help researchers through the data analysis process, whether that is by applying machine learning methods or visualizing data. It is an open-source program (meaning you can download it for free!) and uses a graphical user interface (GUI) that allows the user to perform their analysis by matching icons to one another instead of having to write code.

How it Works

Orange works by using a series of icons known as widgets to perform the various functions that a user would otherwise need to manually code if they were using a program such as Python or R. Each widget appears as a bubble that can be moved around the interface. Widgets are divided into various categories based on the different steps in the analysis process. You can draw lines between the widgets to create a sequence, which will determine the process for how that data is analyzed (which is also known as a workflow). In its current state, Orange contains 96 widgets, each with different customizable and interactive components, so there are many opportunities for performing different types of basic data analysis with this software.

To demonstrate, I will use a dataset about the nutrition facts in specific foods (courtesy of Kaggle) to see how accurately a machine learner can predict the food group a given item falls in based on its nutrients. The following diagram is the workflow I designed to analyze this data:

This is the workflow I designed to analyze a sample sheet of data. From left to right, the widgets placed are "File," "Logistic Regression," "Test and Score," and "Confusion Matrix."

On the left side of the screen are different tabs that each contain a series of widgets related to the task at hand. By clicking on the specific widgets, a pop-up window appears that allows you to interact with the widget. In this particular workflow, the “file” widget is where I can upload the file I want to analyze (there are a lot of different formats you can upload too; in this case, I uploaded an Excel spreadsheet). From there, I chose the machine learning method that I wanted to use to classify the data. The third widget tests the data using the classification method, and compares it to the original data. Finally, the results are visualized through the “confusion matrix” widget to show which cases the machine learner accurately predicted and which ones it got wrong.

A confusion matrix of the predicted classification of food items based on the amount of nutrients in them compared to the actual classifications .

The Limitations

While Orange is a helpful tool for those without a coding background, this system also presents some limitations when it comes to performing certain types of data analysis. One way Orange tries to reconcile this is by providing a widget where the user can insert some Python script into the workflow. While this feature may be helpful for those with a coding background, it would not really impact those who do not have a coding background, thereby limiting the ways they can analyze data.

Additionally, although Orange can visualize data, there are not many features that allow users to adjust the visualization’s appearance. Such limitations may require exporting the data and using another tool to create a more accessible or visually appealing data visualization, but for now, Orange is quite limited in this capacity. As a result, Orange is an incredibly useful tool for basic data visualization but struggles with more advanced types of data science work that may require using other tools or programming to accomplish.

Final Remarks

If you are looking to get involved in data analysis but are just starting to develop an interest in coding, then Orange is a great tool to use. Unlike most data analysis programs, the user-designed interface of Orange makes it easy to perform basic types of data analysis through its widgets. It is far from perfect though, and a lack of a coding background is going to limit the ways you can analyze and visualize your data. Nevertheless, Orange can be an incredibly useful tool if you are just starting to learn how to code and looking to understand the basics of data science!

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