It’s not you. Data Science is tough to learn. It’s getting harder and harder to keep track of advancements in the field while learning the prerequisites. This article has you covered, even if you’re a beginner.
Disclaimer: Some of the resources you’ll find below are free. Those that aren’t are presented through affiliate links. It doesn’t mean anything to you, but I’ll get a small commission if you decide to buy.
Let’s get one thing out of the way. 90% of online materials are good enough. It’s the lack of structure that makes you switch from one to the other and eventually quit.
Many resources state you should be or become a math wizard before diving into data science. That’s some serious BS. Don’t take my word for it – think logically instead. If a junior data scientist should know everything, what’s the point of seniors and team leads?
You see, the basics are enough to get you started. There’s a several year time span between junior and senior position. You’ll have enough time to master everything.
As a beginner, you need one thing – a solid plan. And this article provides it for you, irrelevant of your background. It boils down to three essential points. Learning these will help you get a job as a data scientist even if you don’t have a college degree:
Math and stats
Yes, important stuff, I know. But don’t take it too seriously. It’s a junior position you’re chasing. You should learn the basics of linear algebra, calculus, statistics, and probability.
Yikes. Sounds like a lot, but once again – basics and a good intuition is all you need. Besides, computers do the heavy lifting. You should know how to frame the problem and interpret the results, nothing else.
Best of all, you can learn it free of charge. I’ve watched and enjoyed math courses at Khan Academy before and during my data science education. Here are the ones I recommend:
Also, I’m a big fan of Head First books, and they have a great one on statistics – Head First Statistics. You can read an in-depth review here:
Please, don’t spend the next three months learning math for 10 hours a day. You’ll die in the process.
Instead, mix things up with programming and machine learning materials. The math part was boring AF for me. Life’s too short to be bored to death with theorems and equations. Take it easy – you have years to master these concepts.
Focus on programming and machine learning; that’s where all the fun is.
Python and programming
Nobody cares for your math skills if you can’t code a simple FizzBuzz algorithm. Programming is at the core of data science because it allows you to tell the computer what to do with data.
How to prepare data, train models, and make predictions – all implemented through code. Knowing how to write good-quality code is, hence, mandatory.
Python is considered a go-to language for data science and machine learning. It is easy to learn but tough to master – so you’ll need a couple of years to become an expert.
The Head First book series offers something for Python too. Their Head First Python is by far the easiest for complete beginners. If you still find it too advanced, their Head First Learn to Code is something even your grandma could understand.
If you want to learn more advanced programming concepts, look no further. These three are a must to take your programming skills to the next level:
- Hands-On Data Structures and Algorithms with Python
- Mastering Python Design Patterns
- Elements of Programming Interviews in Python
You can find reviews for all three below:
If you’ve grasped the materials from these three books, I have good news for you – you are job ready! Maybe not in data science, but you shouldn’t have much trouble getting a job as a Python programmer.
Data science and machine learning
You know the basics of programming with Python, and you know Integral isn’t some rare Pokemon. What’s next?
You should dive into data science and machine learning ASAP. Get your hands dirty as soon as you can to see if this field is right for you. There are many learning resources online, both free and paid.
If you want to learn data science and machine learning for free, start with Introduction to Statistical Learning. It was a mandatory read on my data science Master’s for a good reason – it delivers. It’s not the most user-friendly book, and the examples are in R, but the theory sticks after a single reading.
Find ISLR too rigorous? You are not alone. There are simpler books available, especially for complete beginners. Here are my favorites:
- Best data analysis with Python book: Python for Data Analysis
- Best data science and machine learning book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
You can’t go wrong with either, but I find the second one to be better overall. Both cost less than a trip to the movies, so it’s a no-brainer for newcomers.
And that does it – these three concepts are a must to get a junior data science job, even without a college degree. Once again, you don’t have to be an expert. That’s what years of experience will make from you.
Let’s wrap things up in the next section.
To summarize – yes, you can teach yourself data science, and no, it won’t take you years to do so. If you have a solid foundation in math, stats, and programming, it shouldn’t take you more than six months to learn data science. If not, a year should do it.
Both are achievable in 2021, so why don’t you start today?
Remember to mix things up and take it easy. Don’t burn yourself with math. When it feels like it’s too much, take a couple of days off and focus on programming. Take it day by day and use your best judgment.
How did you get your first job as a data scientist? Please share in the comment section.
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