The world’s leading publication for data science, AI, and ML professionals.

The Step-by-Step Curriculum I’m Using to Teach Myself Data Science in 2021

Exactly how I plan on teaching myself data science without spending a single dollar.

Photo by Pavel Herceg on Unsplash
Photo by Pavel Herceg on Unsplash

The end of December is the perfect time to make plans and resolutions for the upcoming new year. For some, resolutions include joining a gym, learning a new language, or reading fifty books.

As for me, my new year’s resolution is to learn Data Science. Because I like a challenge, I want to learn data science without spending a single penny.

Thanks to the generosity and ingenuity of many current data scientists, the internet is teeming with free learning resources covering every minute detail of data science. From learning how to code using Python, to learning multivariable calculus, to learning how to develop machine learning algorithms, it’s now possible to become a competent and competitive data scientist without spending thousands of dollars or years of your time on a post-secondary degree, diploma, or certificate.

There is no better time than a new year to challenge yourself and develop new skills. Plus, if the pandemic has taught us anything, it’s that upskilling is never a waste of time and can help guarantee you better employment options.

Data science is becoming one of the sexiest tech fields to get into, with low unemployment rates and nearly guaranteed job security. Furthermore, data science touches every single industry you could think of, including medicine, the military, business, science, Education, government, and tech. In other words, you can learn data science techniques and apply them to whatever field inspires you or that you already have experience in. What’s there not to like?


Why I want to learn data science.

I graduated with a technical diploma in software engineering two years ago and struggled to get an entry-level job due to a provincial economic recession and because I live in a province that has an allergy to modernizing their economy.

So I’m currently back in school, getting a degree in geoscience. I’m absolutely loving this degree, and lately, I’ve been trying to figure out ways that I can become more valuable and competitive in the industry once I graduate.

Enter, data science. Coupled with my previous experience in software engineering, I believe that adding the data science arrow to my quiver will make me a valuable asset at any environmental or geotechnical company. Plus, having data science knowledge opens the doors for me to start my own environmental consulting company once I graduate (basically, if I can’t get a job, I’ll create my own job). Besides, data is cool and can tell us so much about our past, present, and future, which is especially useful when looking at and trying to understand environmental or geological events.

So why wouldn’t I learn data science?


Some parameters before jumping in.

First, I need to set some parameters before setting up my curriculum.

  • This "new year’s resolution" does not need to be completed within the year. It’s ridiculous to suggest that everyone can learn data science within a year, especially if they have other full-time commitments. This plan will be put in place and I’ll take as long as I need to complete it. I don’t want to rush through this and only have a surface-level understanding. Instead, I want to have a firm grounding in the topics.
  • Programming will be learned first. Because I already have a background in programming (C#, SQL, JavaScript, Java, PHP), I want to learn the new programming languages first. I think this is a great way to step into data science because it lays the foundation for a better understanding of how everything works. Think of Python as the language you speak when doing data science. If I don’t understand the language, I won’t be able to communicate properly. So, I’ll be setting up my curriculum in a manner where I’ll learn the programming languages first before anything else.
  • The courses I use must be free. I’m a student, so I can’t be spending thousands of dollars on online courses. Luckily, there are tons of free courses out there, or courses that allow you to audit them for free. However, there is one exception: as part of the requirements for my science degree, I’m taking a few math courses through my university. These courses will be the only ones I’m paying for. Furthermore, free courses generally focus on the "beginner" side of things, so it’s likely that in the future I would have to pay for more advanced courses.
  • The plan is allowed to change over time. During the course of learning data science, it’s almost guaranteed that I’ll be changing my plan. Whether I’m adding or subtracting courses, the plan needs to be fluid. There’s a high likelihood that the more I learn and educate myself on how best to learn data science, that the courses I take will also change.

The planning resources I’ll be using to create my data science curriculum.

Thanks to the rapidly growing data science community on Medium, I was able to scour the platform for the best resources of information that will help me develop my learning curriculum. From these articles, I modeled my learning path to ensure that I was covering all of my bases.

Here are a few of the articles I found extremely useful while developing my learning curriculum:

How to Learn Data Science for Free

What to Learn to Become a Data Scientist in 2021

Learning Data Science Has Never Been Easier

Data Science Curriculum


The curriculum.

The curriculum will be separated into four learning topics that I will learn in the order presented: programming, mathematics and statistics, data analysis and visualization, and Machine Learning.

Programming

Python

SQL

JavaScript

  • JavaScript Algorithms and Data Structures Certification| freeCodeCamp

Mathematics and Statistics

  • Finite Mathematics (course I’m taking as part of my science degree)
  • Introduction to Statistics (course I’m taking as part of my science degree)
  • Introduction to Calculus I (course I’m taking as part of my science degree)
  • Multivariable Calculus | khan Academy
  • Linear Algebra | khan Academy

Data Analysis and Visualization

Machine Learning

  • TensorFlow 2.0 Complete Course – Python Neural Networks for Beginners Tutorial | freeCodeCamp
  • Practical Deep Learning for Coders – Full Course from fast.ai and Jeremy Howard | freeCodeCamp
  • Reinforcement Learning Course – Full Machine Learning Tutorial| freeCodeCamp
  • Machine Learning with Python Certification | freeCodeCamp
  • Machine Learning with Python: A Practical Introduction | IBM via edX

Data science projects to further my learning.

One thing I learned from my software engineering diploma is that code is best learned through application and practice. So, after I’m done with the learning curriculum (or more likely, while I’m completing the learning curriculum), I’ll be looking to further my learning through capstone projects, Kaggle competitions, and hackathons.

Much like writing, learning to code and to work with data is like exercising a muscle. The more you do it, the easier it gets. In a perfect world, the rule of thumb is to work on personal projects for a few hours on the weekend. Therefore, if I could create some type of habit where I’m working on data science projects every weekend for a few hours, I’d be well on my way to mastering the concepts I worked hard to learn while completing my curriculum.

Here are some of the best resources I’ve found for data science project ideas:

14 Data Science Projects to do During Your 14 Day Quarantine

12 Cool Data Science Projects Ideas for Beginners and Experts

7 Data Science Project Ideas for Aspiring Data Scientists

A Guide To Getting Data Science Projects Ideas


Final thoughts.

This learning curriculum isn’t perfect by any means and is likely to be subject to change the further I progress and learn. However, this curriculum is sure to help me attain a basic level of understanding of data science which I can build on in the future.

Like I said previously, the new year is the perfect time to embark on a new journey. Unfortunately, I like to do everything the hard way, so of course, I couldn’t just be content with going to the gym more or reading some insane number of books. Nope, I had to choose something difficult like teaching myself data science. Thankfully, I’m well versed in the marathon-like experience of learning to code, and I’m comfortable with teaching myself difficult topics (thanks to university being completely online). Luckily, I’ll be entering a welcoming and encouraging community that will likely be instrumental in helping me along in my journey towards data science.

The only thing to do now is to jump in headfirst.


Related Articles