Last week, I shared a framework to help you answer the question, “Should I become a data scientist (or business analyst)?“. For the people, who clear the cut-offs, the next obvious question is “How do I become a data scientist?” In this article, I’ll share what I would have done, if I was starting my journey for a career in data science.
I started my career as an analyst without any knowledge about the tools I was going to work on – all I knew was how to create basic models in Excel. I had not heard about Pivot tables and didn’t know something like conditional formatting even existed in Excel!
Thankfully, Capital One hired me for my logical thinking and not for the knowledge of the tools, I would need to use. In the following years, by working with several employers, freelancing and doing a few pet projects – I learnt several tools and techniques – SAS, SPSS, R and Python included!
Having said that, if I was starting my career today, would I choose the same path? The answer is NO. I would take up a very different path, than what I did. This path would not only cut out the period of confusions I had, but also uses some of the dramatic shifts which have happened in analytics industry in past few years.
So, I thought, I would share how I would plan out my journey to become a data scientist – if I had to chart out my career path today. Here is how I would plan out my journey (in chronological order):
Step 1: Graduate from a top tier university in a quantitative discipline
Thankfully, this didn’t change much for me. Education makes a huge difference in your prospects to start in this industry. Most of the companies who do fresher hiring, pick out people from best colleges directly. So, by entering into a top tier university, you give yourself a very strong chance to enter data science world.
Ideally I would take up Computer Science as the subject of study. If I didn’t get a seat in Computer Science batch, I’ll take up a subject which has close ties with computational field – e.g. computational nueroscience, Computational Fluid Dynamics etc.
This is probably the biggest change, which would happen in the journey, if I was passing out now. If you spend even a year studying the subject by participating in these open courses, you will be in far better shape vs. other people vying to enter the industry. It took me 5+ years of experience to relate to the power R or Python bring to the table. You can do this today by various courses running on various platforms.
One word of caution here is to be selective on the courses you choose. I would focus on learning one stack – R or Python. I would recommend Python over R today – but that is a personal choice. You can find my detailed views about how the eco-systems compare here.
You can choose your path – but this is probably what I would do:
This is to get some real world experience before you actually venture out. This should also provide you an understanding of the work which happens in the real world. You would get a lot of exposure to real world challenges on data collection and cleaning here.
You should aim to get at least a top 10% finish on Kaggle before you are out of your university. This should bring you in eyes of the recruiters quickly and would give you a strong launchpad. Beware, this sounds lot easier than what it actually is. It can take multiple competitions for even the smartest people to make it to the top 10% on Kaggle.
Here is an additional tip to amplify the results from your efforts – share your work on Github. You don’t know which employer might find you from your work!
I would take up a job in a start-up, which is doing awesome work in analytics / machine learning. The amount of learning you can gain for the slight risk can be amazing. There are start-ups working on deep learning, re-inforcement learning – choose the one which fits you right (taking culture into account)
If you are not the start-up kinds, join a analytics consultancy, which works on tools and problems across the spectrum. Ask for projects in different domains, work on different algorithms, try out new approaches. If you can’t find a role in a consultancy – take up a role in captive units, but seek a role change every 12 – 18 months. Again this is a general guideline – adapt it depending on the learning you are having in the role.
Finally a few bonus tips:
What do you think about this path to become a data scientist? Do you have additional tips, which can help people making their career choices. Please feel free to post these tips below for the benefit of larger audience.
photo credit: Indy Kethdy via photopin cc
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