Data Science Roadmap: What you should learn

Data Science has been a term that is being thrown around a lot in recent years in the industry, given its great potential as a career.

So how should you start your own Data Science journey?

Taking a step back

Let’s take a step back. Data Science is an umbrella term – one which is used very liberally. It doesn’t mean the same thing to one company as it means to another company.

Take a look at the diagram below. It’s important to figure out where exactly you want to be on the diagram.

The middle is more of a generalist Data Scientist. There are also more technical roles like Applied Scientist – someone who is a developer plus a data scientist.

Once you figure this out, your journey will be much simpler as this will keep you from going all over the place and getting lost trying to learn everything.

The First Step

Let’s say you have decided that you want to be a generalist data scientist – someone who has good statistics, machine learning, and business knowledge.

Now you have three options:

1. Traditional degree: You can enroll in a Data Science degree program and follow through with that course.

2. Bootcamp: Bootcamp is like a degree program but in a condensed fashion. Think anywhere between 8 to 12 weeks.

3. Self-Teaching: A lot of people end up going for this route. Think of a bunch of courses, peer mentoring, YouTube videos, and personal projects.

The Roadmap

a)     Building Block of Data Science

It’s easy to get overwhelmed when you start learning data science.

I started out by jumping straight into Python and trying to build models. Big mistake!

Python, SQL, or R are tools to apply data science, not data science itself. So if you really want to learn the field, I suggest you start with Statistics. It’s the fundamental building block of any data field.

It doesn’t matter whether you learn it from a degree, a YouTube playlist, or an online course. It just depends on your learning style. 

One great place to start is the book Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce.

b) Moving Forward

Ask any data scientist the number one tool they use every day and you’re likely to hear them say SQL. Start here.

The next step is learning how to code. Pick any out of Python or R.

Python is highly flexible and more general. R generally comes in handy for more statistical tasks.

Pick something that teaches these tools in reference to data science. This way, you’ll simultaneously be doing some Machine Learning.

c) Practice and show your work

This is perhaps the most important step in your entire data science journey.

Once you have a good grasp of basic concepts, start practicing your coding skills.

You need to know how to solve problems using the syntax you just learned.

Spend a good amount of time-solving SQL and Python/R questions on sites like HackerRank, LeetCode, StrataScratch, etc.

And most importantly, you need to build a portfolio of your projects to showcase.

Start small and simple. And remember to showcase it on your GitHub, LinkedIn, etc.

These are some core skills one needs to build to start out. Of course, the journey doesn’t end here. Data Science means constant learning. 

[1] Practical Statistics for Data Scientists Book

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