Getting Started with Data Science, for the complete beginner

If you found this page, it means two things:

1. You are interested in data science, but aren’t sure where or how to begin.
2. My SEO skills are improving.

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If you found this page, it means two things:

  1. You are interested in data science, but aren’t sure where or how to begin.
  2. My SEO skills are improving.

I might be able to help with #1. I can tell you what I’ve done so far and of those, what worked. Maybe you’ll pick up a tip or two, and I will be able to do the post title justice.

#2… Let’s leave it at that.

You might be wondering what credentials I have to say I can help you out. I don’t have any. I’m a complete data science newb. But, why would I need credentials for wanting to help?

If you know less than I do, I can teach you.

If you know more than I do, you can teach me.

If you and I are on the same level, we can help each other learn.

That’s the idea behind this post.

I won’t go into defining what data science is (and its related terms, such as big data and data analytics). I will assume that your finding yourself here means you’ve already googled all that. What I will do is talk you through what I’ve tried so far that has worked for me, and maybe those would help you too.

If you’re just starting out with Data Science*, I would advise you to:

1. Get a proper overview.

The great thing about data science is that there’s a lot of data about it (current Google hits: 58.6 mil**). The not-so-great thing about data science is finding where to start. I mined the Internet for quite a bit before finding some great resources.

One of those great resources is Springboard’s “Getting Your First Data Science Job(review to follow). I’d recommend it to anyone starting out because,

  1. It’s realistic. The book goes out of it’s way to make sure you have an idea of what being a data scientist in the industry is like.
  2. It’s organized. The content, and the flow of the content, are well thought-of. I especially love the little checklist in the end.
  3. It’s FREE.
2. Have a personal project.

It helps to have an idea why you want to learn data science. Think about a question you want answered that needs data science to solve it.

Maybe you want to get the best price for your next ebay auction. Maybe you want to find out what makes popular music tic. Maybe you run a blog and want to find out what topics appeal to your readers *hint*hint*.

Whatever it is, keep it in mind while studying. I found that it keeps me motivated as I can find an immediate use for whatever new thing I’m learning.

3. Study with structure.

Speaking of studying, if you find from the overview that you have a skill gap, you will need to study up. But don’t just study! Study. With. Structure. Have a study plan. Know what you need to study about, how you’ll study it, and what’s a good order to study them in. This increases the chance that you’ll progress, and you’ll have a means of measuring that progress.

For example, I’m currently taking Microsoft’s Professional Program in Data Science via edX. The course structure and timeline are both predictable so I can plan my schedule around them. I can focus on the present course, because I know that someone credible (Microsoft) has already planned what’s next for me. The program and its courses both have progress bars so I can keep myself in check.

I found all this much more effective than say, borrowing a book on R, then wishing it would magically translate to a skill (hint: it doesn’t).

P.S. You can follow along my progress with the Microsoft Program here.

4. Avoid forums.

Information-rich sites like Quora and Kaggle are great if you use them for their purpose. Have a question? Ask Quora. Want to practice your skills? Compete in Kaggle. Simply browsing? Stop. Its easy to get overwhelmed by the information available. It’s easy to doubt the structure you set up for yourself.

Doubt has a large place in data science (hello, statistics!), but you’re just getting started here. You don’t want doubt. You want, no, NEED a map. That’s what the overview in #1 is for, that’s what the structure in #3 is for, that’s what this post is for.

5. Show up.

Once you get started, it will be hard to maintain that inertia. Maybe real life keeps getting in the way, maybe you’re starting to get disinterested, or maybe you just want to give yourself a break.

Whatever it is, have the discipline to follow through. Show up and do the work. Sean Wes has worded this so well at his blog, so I recommend you go over there and inspire yourself.

Personally I’m doing about an hour each day, Monday to Thursday. Sometimes I do more, but I don’t ever do less. If I find that I can’t make it to one of those days, I try to off-set by doing extra hours another day. It’s hard, but it works. I know because I just got my first course certificate last week, and am about half-way to my second one.

That’s it. Nothing new or earth-shattering, just some solid advice. While I had data science in mind here, the overlying tips will apply to getting started at almost anything. I’ll round it out at five because, “5 Tips on Getting Started with Data Science” is perfect SEO material… right? And as with most advice, YMMV. Feel free to suggest some of yours as well.

*I wrote this with self-studying in mind, but I daresay it’s applicable to anyone. 

**This amount of data is just begging for an analysis, in case you’re still looking for your own personal project. When did “data science” as a search term start peaking? What peaked around the same time–is there a correlation? What’s the demographic of the searchers? Is any demographic more statistically significant than the others? And if so, why? Based on these, can you predict what the next generation of data scientists is going to be like?

Image: Solar system and comets in relation to other solar systems, about 1780. Ref F2776.

14 thoughts on “Getting Started with Data Science, for the complete beginner”

    1. Oh! I didn’t know you were taking the program as well. Cool. Hulloh, classmate 🙂

      Just reach out if you need help with any of the exercises!

      On the programming, I’m thinking of leaving that for the start of next year. I’m also considering learning Python outside of the program, as I heard there are some really good Python tutorials out there.


        1. Hi, did you mean where I am wrt the program? Still the same as my response last week: Finished two courses, choosing a 3rd.

          No, I do not have a well-defined plan. My only “plan”, if you’d call it that, is:
          – doing the Microsoft program
          – finding opportunities to exercise data science in my current job role (as an IT project manager, and I’m surprised that there are!)


    1. Hi! I completed the Excel course about a couple of weeks ago, so I guess that puts me at about 2/9 of the DS program.

      I had enrolled for the Power BI course, but found out last week that I won’t be able to do the lab parts with my current laptop.

      I’m still deciding whether I should just audit the Power BI course anyway, or move forward with the Statistics course. Any suggestions?


      1. Hi Danna. I just wanted to ask what your strategy was in regards to learning the content. Do you have fun on excel and practise what you learnt or just leave it for the next day ? I just learn the content and then move one. I know I should practise but I don’t know where to find it ! Lastly , I’m having some difficulty understanding linear regression and t-tests . How should I approach to learning it, what did you do ?


  1. Hi Danna, I’m Avtar. I just stumbled through your blog when I searched for data science. I’m a complete noob on this . I understand your a total novice on this as well. Would be great to see how your coping !


    1. Thanks for the note Avtar. I’ll make sure to keep updating with my progress.

      Right now it’s all about Excel for me–I’m both amazed by how much Excel can do, and frustrated that I can’t get some of it to work!


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