How To Learn Data Science If You’re Broke

How To Learn Data Science If You’re Broke

A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.

Over the last year, I taught myself data science. I learned from hundreds of online resources and studied 6–8 hours every day. All while working for minimum wage at a day-care.

My goal was to start a career I was passionate about, despite my lack of funds.

Because of this choice I have accomplished a lot over the last few months. I published my own website, was posted in a major online data science publication, and was given scholarships to a competitive computer science graduate program.

In the following article, I give guidelines and advice so you can make your own data science curriculum. I hope to give others the tools to begin their own educational journey. So they can begin to work towards a more passionate career in data science.

When I say “data science”, I am referring to the collection of tools that turn data into real-world actions. These include machine learning, database technologies, statistics, programming, and domain-specific technologies.

The internet is a chaotic mess. Learning from it can often feel like drinking from the fun end of a fire-hose.

There are simpler alternatives that offer to sort the mess for you.

Sites like Dataquest, DataCamp, and Udacity all offer to teach you data science skills. Each creating an education program that shepherds you from topic to topic. Each requires little course-planning on your part.

The problem? They cost too much, they don’t teach you how to apply concepts in a job setting, and they prevent you from exploring your own interests and passions.

There are free alternatives like edX and coursera which offer one-off courses diving into specific topics. If you learn well from videos or a classroom setting, these are excellent ways to learn data science.

Check out this website for a listing of available data science courses. There are also a few free course curricula you can use. Check out David Venturi’s post, or the Open Source DS Masters (a more traditional education plan).

If you learn well from reading, look at the Data Science From Scratch book. This textbook is a full learning plan that can be supplemented with online resources. You can find the full book online in pdf form(free), or get a physical copy from Amazon ($27).

These are just a few of the free resources that provide a detailed learning path for data science. There are many more.

To better understand the skills you need to acquire on your educational journey, in the next section I detail a broader curriculum guideline. This is intended to be high-level, and not just a list of courses to take or books to read.

Programming is a fundamental skill of data scientists. Get comfortable with the syntax of Python. Understand how to run a python program many different ways. (Jupyter notebook vs. command line vs IDE)

I took about a month to review the Python docs, the Hitchhiker’s Guide to Python, and coding challenges on CodeSignal.

Hint: Keep an ear out for common problem-solving techniques used by programmers.(pronounced “algorithms”)

A prerequisite for machine learning and data analysis. If you already have a solid understanding spend a week or two brushing up on key concepts.

Focus especially hard on descriptive statistics. Being able to understand a data set is a skill worth its weight in gold.

Learn how to load, manipulate, and visualize data. Mastery of these libraries will be crucial to your personal projects.

Quick hint: Don’t feel like you have to memorize every method or function name, that comes with practice. If you forget, Google it.

Check out the Pandas Docs, Numpy Docs, and Matplotlib Tutorials. There are better resources out there, but these are what I used.

Remember, the only way you will learn these libraries is by using them!

Learn the theory and application of machine learning algorithms.

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