5 tips for getting your first Data Science job in 2020

Many of my followers ask me — How difficult is it to get a job in the Data Science field? Or what should they study? Or what path they should take?

Now the answer is not one everyone would like — Getting into Data Science is pretty difficult, and you have to toil hard.

I mean you have to devote time to learn data science, understand algorithms , upgrade your skills as the market progresses, keep track of old conventional skills, and, of course, search for a job in the meantime and prepare for interviews .

You also have to understand business problems and develop the acumen to frame business problems as data science problems. Remember, there are no fixed algorithms.

It gets really exerting for some and almost impossible for others.

To tell you about myself, I get bored quickly if I am not learning new things. I like Data Science as it gives me that opportunity.

So first of all, I would like to ask if you are like that?

If you are, and you are interested in solving new problems almost every day, then you would love data science as a field to make your career in.

And here are some tips for you brave ones.

1. Start Small

It is better to take many small steps in the right direction than to make a great leap forward only to stumble backward — Old Chinese Proverb

Now, as far as beginning a career in Data Science goes, the above fits pretty nicely. More so if you are coming from a different stream(read not Computer Science, Statistics) or if you want to make a lateral switch.

I would advise against targeting big companies like Amazon, Google, etc. This is not to discourage you; it is more on the lines of practical thinking. I have observed their interview process, and I can assure you that it’s pretty rare, if not impossible, to get these jobs without some experience.

But, let me also tell you that there is no shortage of opportunities. You could easily get into a startup if you know your stuff. That is how I started myself.

2. Keep Learning

I made it my goal to move into the data science space somewhere around in 2013. From then on, it has taken me a lot of failures and a lot of effort to shift jobs. Here is my story if you are interested.

In college, I spent a lot of my time gaming. From 2013 onwards, I spent whatever time I could find to study new technologies and learning about data science.

Nothing will work unless You do — Maya Angelou

Here is the way that I took to learn about data science, and any aspiring person could choose to become a self-trained data scientist.

<strong>How did I learn Data Science?</strong>

I hope that you don’t lose hope after seeing the long list. I already told you it wouldn’t be easy.

You have to start with one or two courses. The rest will follow with time. Just remember that time is a luxury you can afford.

3. Create your Portfolio

Having a grasp of the theory is excellent, but you really don’t add value as a data scientist if you can’t write code.

So work on creating stuff. Try out new toy projects. Go to kaggle for inspiration. Participate in the discussion forums. But don’t stop there.

Think creatively. Build your GitHub profile. Try to solve different problems.

For example, in the starting phase, I created a simple graph visualization to discover interesting posts in DataScience Subreddit using d3.js and deployed it using Flask, and Heroku. I also created a Blackjack Simulator apart from solving the usual data science problems. I also implemented a code-breaking solution using MCMC.

I also took part in various kaggle competitions , and though I don’t have much of a rank to show for it, but I ended up learning a lot.

4. Blogging?

This is something that comes from a personal bias of mine.

When you blog, you end up creating high quality content for others to learn, document your learnings, understand concepts better by explaining them and maybe gain some extra recognition. What else would you want?

Honestly, I love to write, and this is not a pure requirement to become a data scientist, but it helps a lot. I noticed that I understood data science concepts much better when I explained them. And Blogging is a perfect tool for this.

Also, Data Science is pretty vast, and I tend to forget whatever I learned some time ago. Blogging solves this problem too. It was in 2013 that I started my blog and tried to update it with whatever I learned. And thus, I ended up documenting everything. I still consult my blogs whenever I feel stuck on some problem.

I feel that blogging also helped me with my communication skills as it forced me to explain difficult concepts in simpler words.

Anyway, if you don’t like to blog, you can achieve something similar by taking notes.

As I said, Blogging is a personal preference. And if you are interested and want to know how I started writing on medium, here is my story.

<strong>My Data Science Blogging Journey on Medium till now</strong>

5. Don’t be too choosy

You have an offer from an analytics company, and you are thinking if, by joining it, you are saying goodbye to data science.

It is a reasonably good situation to be in. While it is relatively hard to get a data science job, it might be easier to get a job as a business analyst or data analyst in an analytics company.

I would suggest taking any job relating to analysis or reporting or something related to data. I started the same way as I began to work with analytics and switched tracks when the data science opportunity presented itself.

Being in the vicinity of data itself will open you to such opportunities inevitably. Treat your first job just as a stepping stone.

Once you get such a job, you will have two options:

• Make an internal shift in the same company in the Data Science teams by creating good relationships and by showing interest, or

• Continue your learning in your spare time, and keep giving interviews .

With time you would succeed. Good luck to you.

Thanks for the read. I am going to be writing more beginner-friendly posts in the future too. Follow me up at <strong>Medium</strong> or Subscribe to my <strong>blog</strong> .