#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. In 90% of cases, the skills that they teach you at the universities are not really useful in real life data science projects. As I’ve written about several times, in real projects these 4 data coding skills are needed:

bash/command line

Python

SQL

R

(and sometimes Java)

source: KDnuggets

Which 2 or 3 you’ll find most helpful really depends on the company… But if you’ve learnt 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 hobby project or anything!

I wrote a step-by-step article recently on how to install these tools on your computer. Check it out here.

#2: How to learn?

There are 2 major ways to learn data science easily and cost-efficiently.

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.

Top 7 data books I recommend

2nd: Online webinars and video courses.

Data science online courses are coming with fair prices ($10-$500) and they cover various topics ranging from data coding to business intelligence. If you don’t want to spend money on this at the beginning, I’ve listed free courses and learning materials in this post.

(3rd: The Junior Data Scientist’s First Month course

I have created a 6-week online data science course for aspiring data scientist’s to practice and solve true-to-life tasks on a true-to-life dataset: The Junior Data Scientist’s First Month.)

#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 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, here are some of my pet projects from the past few years:

I built a script that monitored a real estate website and emailed me the best deals in real time — so I could get these deals before everyone else.

I built a script that was pulling all the articles form ABC, BBC and CNN and, based on the words used, connected the articles that were about the exact same topic on the 3 different news portals.

I built a self-learning chatbot in Python. (It’s not too smart though — as I haven’t trained it yet.)

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 got back the answer in 7 minutes. The only thing I needed to do was copy-paste the code into my production code and boom, it just worked!

(Note: Cross Validated is another great forum for Data Science related questions.)

+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 do you 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 focusing on big money or on a 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 align with this idea, because people there are usually too busy with their things, so they won’t have time or/and motivation to help you improve (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 to help and teach you.

Okay, you have found some good companies… How to apply? Some principles for your CV: highlight your skills and projects, not your experience (as you don’t have too many years to put on paper yet). List the relevant coding languages (SQL and Python), you use, and link some of your related github repos, so you can show that you really have used that language.

Also, in most cases, companies ask for a 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 in your first few weeks if you were hired. (E.g. “Looking at your registration flow, I’d guess the ____ webpage plays an important role. In my first few weeks, I’d perform ___, ___ and ___ (specific analyses) to prove this hypothesis and understand it more deeply. 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 be any problem to make it happen! Good luck with that!

If you want to try out, what it is like being a junior data scientist at a true-to-life startup, check out my 6-week online data science course: The Junior Data Scientist’s First Month!

And if you want to learn more about data science, check my blog (data36.com) and/or subscribe to my Newsletter! And don’t miss my new coding tutorial series: SQL for Data Analysis!

Thanks for reading!

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Tomi Mester

author of data36.com

Twitter: @data36_com