How do I become a Data Scientist?
The #1 question people ask me is “How do I become a data scientist?”.
So in this post I will tell you all about resources where you can learn data science, where I continue learning and how I stay up-to-date within the field.
How I started out
My path to becoming a data scientist hasn’t been linear. I did my Bachelor in Social Sciences, did my propaedeutic year at the Design Academy Eindhoven, started a premaster in Sociology (where I realized that even though I suck at statistics, I loved the puzzling aspect) only to end up doing a Master in Data Science and graduating there in 2016.
It’s been a messy road. But all of my experiences help me in my data science work.
Almost everything that I learned during my Master’s is outdated by now, but it definitely helped me with a good foundation in mathematics, computer science and even law.
But the point is: I do not have the ‘traditional’ computer background. And I firmly believe that you don’t need one either.
Where to start
Data Science is a mixture of many fields, among which mathematics and computer science. A quick internet search already shows you the many opinions people have about which skills a data scientist needs.
But do not be intimidated by this! In my opinion you need a foundation in computer science basics (mainly learning to program in Python or R) and have an understanding of some mathematics that are used in algorithms. Depending on the projects you’ll be working on you will need to specialize. After all, working with language models requires different skills than building an economical forecast model.
To start out, I’d recommend the following courses:
There is a review of this course that I’ve written which takes you through the content. It’s a great course because it takes you through many facets of data science, as well as teach you the programming language Python.
All in all it forms a good basis.
First of all: Andrew Ng is an absolute legend.
Second: during my Master’s we had to follow this course online. So it was actually part of my curriculum.
It can be overwhelming at times, but this course is the best one out there about Machine Learning.
Speaking at events is also a great way to stretch your knowledge!
Staying up-to-date
There is so much happening in the field that it’s really hard to stay on top of everything. Throughout the years I’ve found a way that works for me, so see this as a way to get started:
Twitter
Yes, yes, I know. To be honest I thought Twitter was dead when I joined Microsoft. But I was very wrong!
It is one of the best places to stay up to date on what’s happening in the field and get direct information from data science leaders. LinkedIn can do the same trick, but I found that the amount of ‘bullshit bingo’ posts on there is much larger.
On my own Twitter page you can find the people that I’m following (tip:I retweet posts with groundbreaking information). It takes a while to build a good network but it’s totally worth it.
Gems on there are: Timnit Gebru, Francois Chollet, Kareem Carr and Towards Data Science.
‘Machine Learning Street Talk’ by Tim Scarfe
This YouTube channel is amazing! Tim talks with data science researchers and discusses their papers and its implications. His video’s always get me thinking and they’re a great way to stay posted on the academic papers out there.
Another YouTube channel about academic papers, but not like you’d expect! In video’s of two minutes this channel will talk you through the newest and most groundbreaking researches out there. For the lazy (or extremely busy!) data scientist I would highly recommend this channel.
‘This Week In Machine Learning’ is a podcast/YouTube channel that goes deep on data science with leading experts. They are a great place to skill up on specific topics as well as be updated on new developments in the field. You can find them on their website as well as on Spotify.
They also have an annual conference which is worth attending.
Extra Resources
Events
There are many, many events being hosted on data science. Just take a look on your timelines on LinkedIn or Twitter.
Your network is always the most reliable indicator of whether a conference is worth your time. And with the ever-changing landscape of virtual events (thank you 2020) it is difficult to provide you with a list.
Specific conferences I always try to attend are:
MeetUp.com – you can find events near your location (also great to network!)
Books
‘The Signal and the Noise’ by Nate Silver.
‘Dataclysm: Who We Are’ by Christian Rudder.
‘Data Science from Scratch’, or any of the other Data Science books from O’Reilly
Every year I speak at the Techionista Academy. Sharing your knowledge is important, and reverse mentoring can help you as well!
Organizations
There are some amazing organizations out there that can teach you about data science. Take your time to double check what the focus of their courses are, and take the leap!
Two organizations (both Netherlands-based) which I definitely recommend are:
Techionista Academy
Been actively involved with this organization since 2017, giving workshops and coaching some of the ladies there.
They re-train women from different fields into data scientists. Their program is Azure focussed (which is an extra plus on your CV) and they have classes starting every 6 months.
Do Good Only Company’s SkillsUP Lab
This company was co-founded by one of my ex-colleagues. It also focusses on training people to become data scientist, with a focus on working ethically with data.
Sharing is caring!
Do you have any good resources? Share them below, or menion me! I will update this blog post with your amazing tips.