The Data Scientist’s Toolbox

List of online courses to improve your job prospects or earn a raise. Many are free or offer need-based scholarships.

Beyond the obvious benefits (making yourself more employable, shifting career paths, etc.), taking online courses can do a lot of good in other ways. If you struggle with work ethic or motivation, taking online courses can help train better habits and build confidence. If you're shy about learning from a professor or with other students around, you can learn in private and without that anxiety. If you're overwhelmed with working two jobs or irregular work schedules, setting aside even a short amount of time every day to do a lesson can add reliability and habit to your life.

Personally, I find when I put even a little energy into learning something (when I might otherwise have browsed social media or played a game), it's left me feeling more motivated for the rest of my day. And the sense of accomplishment that comes from learning something actively can be powerful.

GENERAL EDUCATION SITES

EdX - Free online courses from major universities in a wide variety of topics (economics, biology, programming, business, etc.).

Khan Academy - Free online courses in a lot of topics. This was started as a way for a guy to teach his nephew through Youtube videos and turned into a whole big online education system now. It's largely focused on high school, but includes AP (advanced placement) level courses, college prep, and test prep (SAT, MCAT, GMAT, IIT JEE, NCLEX-RN).

Coursera - Financial Aid is available. Wide variety of courses often linked to universities and many offer certifications. [Coursera Financial Aid info] (https://learner.coursera.help/hc/en-us/articles/209819033-Apply-for-Financial-Aid)

DATA SCIENCE / PROGRAMMING

I'm listing data science / programming courses specifically because these are good programs and because programming, data science, and similar fields are ones where employers often care less about degrees than they do about skills.

Coursera Data Scientists Toolbox - Financial Aid is available. Data science, data analysis, programming, etc.

Dataquest - Free with limits or Subscription. Learn data science, data analysis, data visualization, python, data engineering, etc. Some free lessons, subscription based access to most of the content, but no limits on how fast you go. If you are motivated, you could work through a lot of lessons quickly and make it very affordable.

Code Academy - Free with some limits, or Subscription. Learn Python, HTML, Web design, SASS, and more. Many courses are free. Projects and quizzes require a subscription. They also offer 'Intensive' programs.

LANGUAGES

If you're looking for work in a second language, or if a second language may help your job search, or you simply want a more fun way to get in the habit of learning something new, check out:

Duolingo- Free language learning with a really easy and comfortable interface, especially when used on a computer rather than a mobile device. Completing a course won't make you fluent, but will give you a working level ability.

Mango - Free (through library). Language learning is often free with your local library card.

Mondly - Free with limits or Subscription, but has many languages including some that Duo and Mango don't have and they often offer discounts on their monthly or annual fees.

I'd echo what some other people in this thread are saying and recommend starting with R. Its organization in data tables is much more analogous to Excel's data organization, and RStudio makes the connection between the figures you produce and the code much easier to connect in your head.

I use both R and Python (and C++, and Java, and Bash...) in my day-to-day work, and while Python has very powerful machine learning capabilities and is invaluable in data cleaning and organization tasks, it can be a little tougher to learn good data organization and analysis practices. Johns Hopkins offers a (free?) set of online data science courses that do a good job of getting you up and running and demonstrate a lot of the powerful things you can do with a data-science-oriented programming language.

Once you have the ideas down from R, it's pretty easy to start reaching out to other languages for tools you need.

The Data Science Toolkit on Coursera is amazing. Teaches SQL and Python iirc.

Edit: https://www.coursera.org/learn/data-scientists-tools

Maybe a free online course, for example this one https://www.coursera.org/learn/data-scientists-tools

https://www.coursera.org/learn/data-scientists-tools

A co-worker took all ten and it has been incredibly helpful for him, you get a certification form John-Hopkins if you do all ten and complete the capstone

cool thanks for that, i went to this url after googling it https://www.coursera.org/learn/data-scientists-tools?specialization=jhu-data-science

it appears as tho its a paid course? like going to univiersity, but its online. am i going to the wrong link?

Try classes on Coursera!

I took many classes in Johns Hopkins Data Science Specialization during my PhD, to learn R.

Here's the first one: https://www.coursera.org/learn/data-scientists-tools

Building a Course-Series to prepare for PhD [Self-Teaching, Statistics, General Math]

Hi. I have a bachelors in Politics/History and Writing, and now I want to pursue a PhD in the hard sciences, but I need to put some work into recovering/improving my math literacy before it makes sense to apply. I would like to set up a personalized course-series like this one.

I am pretty sure I want to start with this course; but I don't want that to falter because of my atrophied algebra knowledge, for example.

My Goals

• To shore up weak points in my atrophied math knowledge
• To study the math needed to be considered a serious PhD candidate
• To comprehend data science, probability, and statistics
• To be able to use Bayesian statistics
• To be able to interact with raw data, to analyze and explain it
• To be able to use R to do that analysis

What I'm looking for from you:

• Help identifying free or affordable sources to learn the above
• Help determining the order and time-frame of my project
• Help codifying the scope of knowledge I'm looking for (how many subjects do I need to master to look like a good candidate? What can I reasonably skip?)

Some Background About Me: I'm specifically interested in working with Dan Kahan out of Yale Law. He's doing work on what he calls the Science of Science Communication, (Why is there persistent public doubt in scientific consensus? How can understanding that help us communicate better about science?) I have done plenty of work studying how scientific knowledge progresses, decision theory, heuristics and bias-psychology and the like. I am even comfortable talking about statistics (including explaining and applying Bayes'), but I can't do the math myself. I can read and interpret scientific literature, and even follow along with the math (I have opinions about P-Hacking, for example) but I could not do the experiments or data analysis myself.

Hi there,

I think you'll benefit from taking a look to this portion of this totally free course

And the following: Getting help for data analysis how to ask question the smart way

We understand you are in need, but this is probably the worst way to ask for it and to learn from it. Get your stats foundation right first, read the documentation, show us your code, and then we will be more than happy to help.