Data Science
Foundations using R

https://www.coursera.org/specializations/data-science-foundations-r

I made this course it is very good but also hard. You can choose free or pay.

Coursera offers this free class that John Hopkins put together. There is a series of different classes relating to statistics. By not paying for the class, you are able to see the material but if I recall, you can't be graded on some of the evaluations or maybe can't participate in the online discussion forums? One of those.

I learned how to use R from the Hopkins Coursera course years ago. Now, R is my go-to software. I think this is their updated version of it: https://www.coursera.org/specializations/data-science-foundations-r

When I TA intro methods courses that use R, I recommend students download the swirl package: https://swirlstats.com/ It is less intense than a Coursera course and can help folks pick up some bare-bones basics. There are also more advanced lessons for folks who want them.

I am interviewing for an institutional research position at a college. I'm an admin by trade and self-taught in R. I want to brush up on my R and the interviewer said it would help to know how to "transform and analyze" data in R. Would this series of courses be good for brushing up? Thanks,

https://www.coursera.org/specializations/data-science-foundations-r#courses

First of all, congrats for your choice ;) I'm doing bioinformatics right now and is important to have a minimum knowledge on programming, even for running someone's else software. You can be a basic bioinformatician and perform analysis using available software (it's okay and they're necessary too because sometimes it's hard to do it and to interpret the results) or you can be develope your own software and analysis. In this case, you really have to know how to program.

It's weird that your university doesn't teach you basic programming, maybe it's not specified but you will do. If not, maybe you should think about going to another university.

In bioinformatics the most used programming languages are Python and R (most for the statistics part), and you should also be used to work with the Linux command line. There's a lot of online courses on coursera to learn this.

Python: https://www.coursera.org/learn/python (I did this one when I started) https://www.coursera.org/learn/python-genomics? https://www.coursera.org/learn/bioinformatics https://www.coursera.org/specializations/bioinformatics

R: https://www.coursera.org/specializations/data-science-foundations-r?

Linux: https://www.edx.org/course/introduction-to-linux

Enjoy the journey! Programming could be really frustrating sometimes, but hold on! And the end it's really fulfilling. Hope it helped :)

Machine Learning

Data Science: Foundations using R Specialization

Data Science: Statistics and Machine Learning Specialization

(I'm not sure why it got removed maybe bc it was self promotion perhaps?)

I was actually in your shoes a little while back (starting my PhD with no real coding experience). Funnily enough, I also wanted to learn about coding with an emphasis on bioinformatics (Python & R). For that, I've been working through a couple of Coursera specializations. The main reason I chose Coursera was so that I could put the completion certificates on my LinkedIn, which I'm hoping gives me at least some minor legitimacy over someone just *claiming* to have experience.

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If you're looking for specifics, here is a list of the specializations I'm using:

• Python for Everyone
• Applied Data Science with Python
• Data Science: Foundations using R
• Bioinformatics

Planning on doing these Coursera courses in this order:

https://www.coursera.org/specializations/data-science-foundations-r

https://www.coursera.org/specializations/introduction-data-science

https://www.coursera.org/specializations/applied-data-science

https://www.coursera.org/specializations/ai-foundations-for-everyone

https://www.coursera.org/specializations/data-science-statistics-machine-learning

https://www.coursera.org/specializations/ibm-intro-machine-learning

https://www.coursera.org/specializations/deep-learning

https://www.coursera.org/specializations/advanced-data-science-ibm

I want to become proficient enough in Data Science to be able to have it as a "tool in my toolbox." What are your thoughts on my self-teaching course plan?