What’s In A Name?: From Lynda.com to LinkedIn Learning

LinkedIn Learning Logo

Lynda.com had a long history with libraries. The online learning platform offered video courses to help people “learn business, software, technology and creative skills to achieve personal and professional goals.Lynda.com paired well with other library services and collections, offering library users the chance to learn new skills at their own pace in an accessible and varied medium. 

However, in 2015—twenty years after its initial launch—Lynda.com⁠⁠ was purchased by LinkedIn. A year later, Microsoft purchased LinkedIn for $26.2 billion. And now, in 2019, Lynda.com content is available through the newly-formed LinkedIn Learning.

Charmander evolving into Charmeleon

Sometimes, evolution is simple (like when it gets you one step closer to an Elite-Four-wrecking Charizard). Sometimes, it’s a little more complicated (like when Microsoft buys LinkedIn which just bought Lynda.com).

The good news is that this change from Lynda.com to LinkedIn Learning includes access to all of the same content previously available. This means that, through the University Library’s subscription, you still have access to courses on software like R, SQL, Tableu, Python, InDesign, Photoshop, and more (many of which are available to use on campus at the Scholarly Commons). There are also courses on broader, related topics like data science, database management, and user experience

Setting up your own personal account to access LinkedIn Learning is where things get just a little trickier. As a result of the transition from Lynda.com to LinkedIn Learning, users are now strongly encouraged to link their personal LinkedIn accounts with their LinkedIn Learning accounts. Completing courses in LinkedIn Learning will earn you badges that are automatically carried over to your LinkedIn account. However, this additional step—using a personal LinkedIn account to access these course—also makes the information about your LinkedIn Learning as public as your LinkedIn profile. Because Lynda.com only required a library card and PIN, this change in privacy has received push-back from libraries and library organizations across the country.

Obi-Wan Kenobi looking confused with caption reading [visible confusion]

This new policy change doesn’t mean you should avoid LinkedIn Learning, it just means you should use it with care and make an informed decision about your privacy settings. Maybe you want potential employers to see what you’re proactively learning about on the platform, maybe you to keep that information private. Either way, you can get details on setting up accounts and your privacy settings by consulting this guide created by Technology Services.

LinkedIn Learning can be accessed through the University Library here.

DIY Data Science

Data science is a special blend of statistics and programming with a focus on making complex statistical analyses more understandable and usable to users, typically through visualization. In 2012, the Harvard Business Review published the article, “Data Scientist: The Sexiest Job of the 21st Century” (Davenport, 2012), showing society’s perception of data science. While some of the excitement of 2012 has died down, data science continues on, with data scientists earning a median base salary over $100,000 (Noyes, 2016).

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

LibGuides to Get You Started:

And more!

There are also a lot of resources on the web to help you:

The Open Source Data Science Masters

  • 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

Dataquest

  • 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

David Venturi’s Data Science Masters

  • 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

Mitch Crowe Learn Data Science the Hard Way

  • 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!

Works Cited:

Davenport, T. H., & Patil, D. J. (2012, October 1). Data Scientist: The Sexiest Job of the 21st Century. Retrieved June 1, 2017, from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
Noyes, K. (2016, January 21). Why “data scientist” is this year’s hottest job. Retrieved June 1, 2017, from http://www.pcworld.com/article/3025502/why-data-scientist-is-this-years-hottest-job.html

Learn Python Summer 2017

Are you sitting around thinking to yourself, golly, the bloggers at Commons Knowledge have not tried to convince me to learn Python in a few weeks, what’s going on over there? Well, no worries! We’re back with another post going over the reasons why you should learn Python. And to answer your next question no, the constant Python promotion isn’t us taking orders from some sinister serpentine society. We just really like playing with Python and coding here at the Scholarly Commons.

Why should I learn Python?

Python is a coding language with many applications for data science, bioinformatics, digital humanities, GIS, and even video games! Python is a great way to get started with coding and beef up your resume. It’s also considered one of the easier coding languages to learn and whether or not you are a student in LIS 452, we have resources here for you! And if you need help you can always email the Scholarly Commons with questions!

Where can I get started at Scholarly Commons?

We have a small section of great books aimed at new coders and those working on specific projects here in the space and online through the library catalog. Along with the classic Think Python book, some highlights include:

Python Crash Course: A Hands on Project-Based Introduction to Programming

Python Crash Course is an introductory textbook for Python, which goes over programming concepts and is full of examples and practice exercises. One unique feature of this book is that it also includes three multi-step longer projects: a game, a data visualization, and a web app, which you can follow for further practice. One nice thing is that with these instructions available you have something to base your own long term Python projects on, whether for your research or a course. Don’t forget to check out the updates to the book at at their website.

Automate Boring Stuff with Python: Practical Programming for Total Beginners

Automate Boring Stuff with Python is a solid introduction to Python with lots of examples. The target audience is non-programmers who plan to stay non-programmers; the author aims to provide the minimum amount of information necessary so that users can ultimately use Python for useful tasks, such as batch organizing files. It is still a lot of information and I feel some of the visual metaphors are more confusing than helpful. Of course, having a programming background helps, despite the premise of the book.

This book can also be found online for free on this website.

Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code

Although focused on Python 2, this is a book about teaching programming skills to newbie coders. Although the author does not specifically use this term this book is based on what is known in psychology as deliberate practice or “the hard way,” which is described in Cal Newport’s blog post “The Grandmaster in the Corner Office” (Newport, 2010).  And Learn Python the Hard Way certainly lives up to the title. Even the basic command line instructions prove difficult. But based on my own learning experiences with deliberate practice, if you follow the instructions I imagine you will have a solid understanding of Python, programming, and from what I’ve read in the book definitely some of your more techie friends’ programming jokes.

Online Resources

If the command line makes you scared or if you want to get started right away, definitely check out PythonAnywhere, which offers a basic plan that allows users to create and run Python programs in their browser. If PythonAnywhere isn’t your speed, check out this article, which lists the 45 best places to learn to code online.

Interested in joining an online Python learning group this summer?

Definitely check out, Advent of Python, an online Python co-learning group through The Digital Humanities Slack. It started Tuesday May 30 with introductions, and every week  there will be Python puzzles for you to help you develop your skills. IT IS NOT TOO LATE TO JOIN! The first check-in and puzzle solutions will be June 6. The solutions and check-ins are going to be every Tuesday, except the Fourth of July — that meeting will be on Wednesday, July 5.  There is a Slack, a Google Doc, and subreddits.

Living in Champaign-Urbana?

Be sure to check out Py-CU a Maker/Hacker group in Urbana welcome to coders with all levels of experience with the next meeting on June 3rd. And obligatory heads up, the Urbana Makerspace is pretty much located in Narnia.

Question for the comments, how did you learn to code? What websites, books and resources do you recommend for the newbie coder? 

Works Cited:

Newport, C. (2010, January 6). The Grandmaster in the Corner Office: What the Study of Chess Experts Teaches Us about Building a Remarkable Life. Retrieved May 30, 2017, from http://calnewport.com/blog/2010/01/06/the-grandmaster-in-the-corner-office-what-the-study-of-chess-experts-teaches-us-about-building-a-remarkable-life/