How to get your first job in Data Science?
We provide guidelines for the most important questions, including the key data scientist skills and tools, how to get them, how to learn and practice, and where to send your application.
By Tomi Mester, Data36.
How can one get his/her first entry level job as a Data Scientist/Analyst?
If you scroll through the data science subreddit, you will find many questions around this topic. Readers of my blog (data36.com) asking the same from me time to time. And I can tell you this a totally valid problem!
I have decided to summarize my answers for all the major questions!
#1: What are the most important data scientist skills and tools? And how can you get them?
Good news — bad news.
I will start with the bad one. The skills that they teach you at the universities in 90% of the cases are not really useful in real life data science projects. As I’ve written about that several times, in real projects these 4 data coding skills are needed:
- bash/command line
- Python
- SQL
- R
- (and sometimes Java)
Source: KDnuggets
It really depends on the company, which 2 or 3 they use. But if you’ve learned one, it will be much easier to learn another.
So the first big question is: how can you get these tools? Here comes the good news! All of these tools are free! It means, that you can download, install and use them without paying a penny for them. You can practice, build a data pet-project or anything! I wrote a step by step article recently on how to get and install these tools on your computer. LINK:
Data Coding 101: Install Python, SQL, R & Bash (for non-devs)
#2: How to learn?
There are 2 major sources of learning data science — easily and cost-efficiently. Don’t worry, none of these are to attend €1000+ conferences or workshops.
1st: Books.
Kinda old-school, but still a good way of learning. From books you can get very focused, very detailed knowledge about online data analysis, statistics, data coding, etc… I highlighted 7 books I recommend — in my previous article here: LINK.
Top 7 data books I recommend
2nd: Online webinars and video courses.
Data science online courses are fairly cheap ($10-$50) and they cover various topics from data coding to business intelligence. If you don’t want to spend money on this, I’ve listed free courses and learning materials in this post: LINK.
#3: How to practice, and how to get real life experience?
This is a tricky one, right? Every company wants to have people with at least a little bit of real life experience… But how do you get real life experience, if you need real life experience to get your first job? Classic catch-22. And the answer is: pet projects.
“Pet project” means that you come up with a random data project idea, that makes you excited. Then you simply start to build it. You can think about it as a small startup, but make sure that you keep focusing on the data science part of the project and you can just ignore the business part. To give you some ideas, I listed here some of my pet projects from the past few years:
- I’ve built a script that was monitoring a real estate website and emailed me the best deals in real time — so I could get these deals before everyone else.
- I’ve built a script that was pulling all the articles form ABC, BBC and CNN and based on the used words connected the articles that were about the exact same topic on the 3 different news portals.
- I’ve built a self-learning chatbot in Python. (It’s not too smart though — as I haven’t trained it yet.)
- Etc…
Be creative! Find a data science related pet project for yourself and start coding! If you hit the wall with a coding problem — that can happen easily, when you start to learn a new data language — just use google and/or stackoverflow. One short example of mine — on how effective stackoverflow is:
left side: my question — right side: the answer (in 7 minutes)
Notice the timestamp! I’ve sent in a sort of complicated question and I’ve got back the answer in 7 minutes. The only thing I needed to do after this is to copy-paste the code into my production code and boom, it has just worked!
(UPDATE1: Cross Validated is another great forum for Data Science related questions. Thanks for the addition for nameBrandon from reddit.)
+1 suggestion:
Even if it’s a little bit difficult, try to get a mentor. If you are lucky enough, you will find someone, who works in a Data Scientist role at a nice company and who can spend 1 hour weekly or biweekly with you and discuss or teach things.
#4: Where and how to send your first job application?
If you haven’t managed to find a mentor, you can still find your first one at your first company. This is gonna be your first data science related job, so I suggest not to focus on big money or on super-fancy startup atmosphere. Focus on finding an environment, where you can learn and improve yourself.
Taking your first data science job at a multinational company might not fit in this idea, because people there are usually too busy with their things, so they won’t have time or/and motivation help you improving (of course, there are always exceptions).
Starting at a tiny startup as a first data person on the team is not a good idea either in your case, because these companies don’t have senior data guys to learn from.
I advise you to focus on 50–500 sized companies. That’s the golden mean. Senior data scientists are on board, but they are not too busy helping and teaching you.
Okay, you have found some good companies… How to apply? Some principals for the CV: highlight your skills and projects, not your experience (as you don’t have too much years on your paper yet). List out the data coding languages, you use, and link some of the related github repos of yours, so you can show, that you really have used that language.
Also in most cases companies are asking for cover letter. It’s a good opportunity to express your enthusiasm of course, but you could add some practical details as well like what would you do on your first few weeks, if you’d be hired. (Eg. “Looking on your registration flow, I guess the ____ page plays a great role in it. On my first few weeks, I’d make ___, ___ and ___ specific researches around that to prove this hypothesis and understand it deeper. It could help the company to improve _____ and eventually push the _____ KPIs.”)
Hopefully this would land you a job interview, where you can chat a little bit about your pet projects, your cover letter suggestions, but it will be mostly about personality fit-check and most probably some basic skill-test. If you had practiced enough, you will pass this… but if you are a nervous type and you want to practice more, you can do it on hackerrank.com.
Conclusion
Well, that’s it. I know it sounds easier, when it’s written, but if you are really determined to be a Data Scientist, it won’t cause you any problem to make it happen! Good luck with that!
And if you want to learn more about data science, check my blog (data36.com) and/or subscribe to my Newsletter!
Thanks for reading!
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Original post. Re-posted with permission.
Bio: Tomi Mester is Data analyst & researcher focused on data driven startups, eCommerce and big data analytics.
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