Many aspiring data scientists focus on doing Kaggle competitions as a way to build their portfolios. Kaggle is an excellent way to practice, but it should only be one of many avenues you use to work on data science projects. This is because Kaggle competitions only focus on a narrow part of data science work.
Let’s face it, data science is cool. And since it’s cool, there are lots of great jobs out there for data people. Unfortunately, lots of people who would love data science just don’t know how to land their first data science job.
- Kaggle mostly deals with machine learning, which is only one aspect of Data Science.
- When you work on Kaggle you are dealing largely with pre-cleaned data, so you don’t get enough experience cleaning messy data, which is (colloquially) 80% of what a Data Scientist does.
- Because of the large volume of people entering Kaggle competitions, getting into the top few percents or winning a competition requires not only skill but a lot of time and some luck.
To build your skills more holistically, it’s a good idea to work on your own projects. It’s common to share this code on Github to interest potential employers, but it’s important to be very purposeful in what code you put up and how.
While it’s fast to throw up some code on Github and hope someone looks at it, it’s far more effective in the long run to put time and effort into how you construct and present your portfolio.
Let’s start by examining why a portfolio is effective in the first place.
1. What are the most important data scientist skills and tools? And how can you get them?
The skills that they teach you at the universities in 90% of the cases are not really useful in real life data science projects. In real projects these 4 data coding skills are needed:
- bash/command line
- (and sometimes Java)
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!
2. How to learn?
There are 2 major sources of learning data science — easily and cost-efficiently.
Kind of 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.
2nd: Online Webinars and Video Courses.
Data science online courses are fairly cheap INR 700 – INR 800 at Unanth and they cover various topics from data coding to business intelligence. Some of the Courses are free too.
You can Visit www.unanth.com
3. How to practice, and how to get real-life experience?
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?
The answer is 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.
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 going to be your first data science-related job, so the suggestion is 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 the 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.
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.