Here at the Scholarly Commons, we believe that having a better understanding of statistics means you are less likely to get fooled when they are deployed improperly, and will help you have a better understanding of the inner workings of data visualization and digital humanities software applications and techniques. We might not be able to make you a data scientist (though certainly please let us know if inspired by this post and you enroll in formal coursework) but we can share some resources to let you try before you buy and incorporate methods from this growing field in your own research.
As we have discussed again and again on this blog, whether you want to improve your coding, statistics, or data visualization skills, our collection has some great reads to get you started.
In particular, take a look at:
The Human Face of Big Data created by Rick Smolan and Jennifer Erwitt
- This is a great coffee table book of data visualizations and a great flip through if you are here in the space. You will learn a little bit more about the world around you and will be inspired with creative ways to communicate your ideas in your next project.
Data Points: Visualization That Means Something by Nathan Yau
- Nathan Yau is best known for being the man behind Flowing Data, an extensive blog of data visualizations that also offers tutorials on how to create visualizations. In this book he explains the basics of statistics and visualization.
Storytelling with Data by Cole Nussbaumer Knaflic
- The expanded book version of Cole Nussbaumer Knaflic’s blog and workshop series of the same name, Storytelling with Data is particularly aimed at folks doing business analytics, though most of the strategies cross over into other disciplines. We have the print version but if you can’t make it to the space it’s also available as an ebook through the University Library Online Collection.
LibGuides to Get You Started:
- Qualitative Data Analysis: Your Options for Programs
- Visualizing Your Data
- Finding Social Science Data
- Introduction to Data Management for Undergraduate Students
- Text Mining Tools
There are also a lot of resources on the web to help you:
- This is not an accredited masters program but rather a curated collection of suggested free and low-cost print and online resources for learning the various skills needed to become a data scientist. This list was created and is maintained by Clare Corthell of Luminant Data Science Consulting
- This list does suggest many MOOCS from universities across the country, some even available for free
- This is a project-based data science course created by Vik Paruchuri, a former Foreign Service Officer turned data scientist
- It mostly consists of a beginner Python tutorial, though it is only one of many that are out there
- Twenty-two quests and portfolio projects are available for free, though the two premium versions offer unlimited quests, more feedback, a Slack community, and opportunities for one-on-one tutoring
- A DIY data science course, which includes a resource list, and, perhaps most importantly, includes links to reviews of data science online courses with up to date information. If you are interested in taking an online course or participating in a MOOC this is a great place to get started
- Another curated list of data science learning resources, this time based on Zed Shaw’s Learn Code the Hard Way series. This list comes from Mitch Crowe, a Canadian data science
So, is data science still sexy? Let us know what you think and what resources you have used to learn data science skills in the comments!