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Gold BlogHow to Get Into Data Science Without a Degree


Breaking into any new field or slogging through a career change is always a challenge, and requires focus and even a little grit. While transitioning to becoming a Data Scientist is no different, aspiring to this role is possible, even without a formal post-secondary degree, largely due to the vast amount of quality learning resources available today.



This article is for those who fall into one of the following categories:

  1. You don’t have a post-secondary degree, but you’re interested in data science.
  2. You don’t have a STEM-related degree, but you’re interested in data science.
  3. You’re working in a field completely unrelated to data science, but you’re interested in data science.
  4. You’re simply interested in data science and want to learn more about it.

You’re probably wondering, “Do I even have a chance?”

The answer is, “Yes, it’s possible.”

And the good news is that you’ve already passed the first step, which is that you’re interested in data science. Now it’s not going to be an easy journey because you are an underdog, but use that as fuel to motivate yourself every day.

On top of that, I’m going to give you my advice that I wish I had when I started out.

First, a little bit about myself…

I have a business degree, but I’ve been interested in machine learning since my second year of university. And so, I self-learned most of what I know today, and I’ve been fortunate to work in a few data analyst/data science jobs.

Why am I telling you this? I want to make it clear that I was once in a similar position as you!

Remember that this is a long-term goal, and thus you should expect results in the long-term. If and when you’re willing to commit 100% of yourself, I would give it at least a year before you decide whether to continue or not.

With that’s said, let’s dive into it:

Getting into data science comes down to two things, growing and showing your skills.

 

1) Growing your data science skills

 

Not too long ago, I wrote an article, “How I’d Learn Data Science if I Could Start Over.” In this article, I segmented what to learn by subject, i.e., statistics & mathematics, programming fundamentals, and machine learning.

In this article, I’m going to segment what you should learn by your level of understanding.

Level 0: Fundamentals

You have to start with the basics, the building blocks, whatever you want to call it. But trust me when I say this, the better your fundamentals are, the smoother your data science journey will be.

Particularly, I recommend that you build your fundamentals in the following topics: statistics & probability, mathematics, and programming.

Statistics and Probability: if you’ve read my previous articles, then you’ve probably heard this for the MILLIONth time, but a data scientist is really just a modern statistician.

  • If you have little to no exposure to statistics and math, I recommend Khan Academy’s course on statistics and probability.
  • However, if you have some knowledge of calculus and integrals, I strongly recommend that you go through Georgia Tech’s course called “Statistical Methods.” It is a little more difficult as it goes through more proofs, but it will help you understand the intricacies of each idea.

Mathematics: Depending on how much attention you paid in high school will determine how much time you need to spend learning basic mathematics. There are three areas that you should learn: calculus, integrals, and linear algebra:

  • Calculus is essential when it comes to anything related to optimization (which is quite relevant in data science). I recommend Khan Academy’s course on calculus for this.
  • Integrals are essential when it comes to probability distributions and hypothesis testing. I recommend Khan Academy’s course on integrals.
  • Linear Algebra is especially important if you want to get into deep learning, but even then, it’s good to know for other fundamental machine learning concepts, like principal component analysis and recommendation systems. Surprise surprise, you can guess what course I recommend for this. The link is provided here.

Programming: Just as having a basic understanding of math and stats is important, knowing the core fundamentals in programming will make your life much easier, especially when it comes to implementation. Therefore, I recommend that you take the time to learn basic SQL and Python before diving into machine learning algorithms.

  • If you’re completely new to SQL, I recommend going through Mode’s SQL tutorials, as it’s very succinct and thorough.
  • Similarly, if you’re completely new to Python, Codecademy is a good resource to familiarize yourself with Python.

Level 1: Specialize

Once you’ve learned the fundamentals, you’re ready to specialize. At this point, it’s up to you whether you want to focus on machine learning algorithms, deep learning, natural language processing, computer vision, etc…

There are so many more things that you can specialize in, so please explore more before you make a decision!

Level 2: Practice

Like anything else, you have to practice what you learn because you lose what you don’t use! Here are 3 resources that I recommend to practice and refine your skills.

  1. Leetcode is a great resource that has helped me learn skills and neat tricks that I never thought was possible. It’s something that I leveraged significantly while job searching, and it is a resource that I will always go back to. The best part of it is that they normally have recommended solutions and discussion boards, so you can learn about more efficient solutions and techniques.
  2. Pandas practice problems: this resource is a repository full of practice problems specifically for Pandas. By completing these practice problems, you’ll know how to: filter and sort data, aggregate data, use .apply() to manipulate data, and more.
  3. Kaggle is one of the world’s largest data science communities with hundreds of datasets that you can choose from. With Kaggle, you can compete in competitions or simply take advantage of the datasets available to create your own machine learning models.

 

2) Showing your data science skills

 

Learning data science is one thing, but something that people commonly forget is marketing themselves — you’ll eventually want to show what you’ve learned. This is especially important for you if you don’t have a degree related to data science.

Once you’ve completed a couple of personal data science projects, below are several ways for you to showcase them and market yourself:

Your resume

First, leverage your resume to showcase your data science projects. I recommend creating a section called “Personal Projects,” where you can list two to three projects that you’ve completed.

Similarly, you can add these projects in the “Projects” section on LinkedIn.

Github repository

I strongly recommend that you create a Github repository if you haven’t already. And while we’re on the topic of Github, it would be a good idea to learn Git. Here, you can include all of your data science projects, and more importantly, you can share your code with others to see.

If you have a Kaggle account and create notebooks on Kaggle, this serves as a good alternative as well.

Once you have an active Kaggle or Github account, make sure that your account URL is available on your resume, your LinkedIn, and your website if you have one.

Personal website

Speaking of a website, I strongly recommend building a data science portfolio in the form of a website as well. HTML and CSS are very simple to learn, and it would be a fun project! If you don’t have the time, something like Squarespace will work well too.

Blogging on Medium

I’m biased because this has worked well for me, but that doesn’t mean that I can’t recommend blogging! With a platform like Medium, you can write project walkthroughs, like mine on Wine Quality Prediction.

Non-Profit Opportunities

Lastly, take advantage of non-profit data science opportunities. I came across a resourceful article written by Susan Currie Sivek, which provides several organizations where you can get the opportunity to work on real-life data science projects.

Original. Reposted with permission.

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