What degree of depth and knowledge will be required in each subject to get the job?Good question.
In fact everything is heading towards point 7 (do projects!) So the ideal would be to get there as quickly as possible, with enough knowledge to defend yourself in a good part of the flow for a typical data project.
Well, let’s consider a tactic that will help us to optimize the trip.
These are some of the key points for me:Learn by doing: The best way to learn something is to put it into practice.
Spend most of your time writing code.
And I’ll say it again: do projects!Organize your agenda.
Try to spend some time learning each day.
Set small milestones with realistic deadlines (you can use a board if that helps you) and try to meet them.
Check what you could accomplish and what you couldn’t.
Don’t get overwhelmed, but do not relax either :)Learn as if you had to teach: Take notes, make summaries, draw diagrams… very good! But you don’t really understand something unless you can explain it to your grandmother.
And that’s the reason why I decided to start this blog 🙂 You can follow the Feynman technique.
Take the top-down approach.
With a bottom-up approach, what we would do is to follow the classic flow of learning: learn first all the small pieces before you can reach the whole.
An example of this approach would be to choose an Algebra course, another one for Calculus and one more for Probability and Statistics, with the only purpose of being able to face the Machine Learning algorithms.
With a top-down approach, we’ll simply try to learn Machine Learning, scratching (or deepening) the mathematical part when necessary.
This way we won’t lose the motivation, the focus, or our time with something maybe irrelevant.
Did you learn what an offside is before playing your first soccer game?Be resourceful: there are a lot of available resources and tools (click on the link!).
As important as having a solid knowledge is the ability to quickly locate what we don’t know or don’t remember.
Upload your projects to GitHub.
Those will be your credentials to apply for the job you want.
If you don’t have paid experience, you’ll need to prove experience with your own projects.
On the Internet you can find a lot of ideas or papers, and you can also try to solve a real problem or a concern of your day to day.
Don’t let yourself be drowned by the amount of information published daily.
There are many people doing very interesting and innovative things, but you need to focus on acquiring the base that will enable you to become a data scientist; you can ignore what is published every minute on Twitter.
Prepare the interviews thoroughly.
There’s a lot of information on this (lists of typical questions, tips to improve your CV, …) and even mentors if you need an extra help.
By the way: it is essential that you are able to explain what you did in your data projects.
Remain up to date once the goal is reached.
Subscribe to the most relevant blogs and newsletters, follow the data gurus on Twitter or Linkedin, participate in forums, attend meetups, try to be Gold in a Kaggle competition, or simply expand your skills.
Final tip: the journey is long, so don’t face it as a speed race, but as a half marathon.
Be constant and follow your plan, but dosing your strength.
Surely there will come a time when you think of surrender, but that’s also part of the process.
In the end, as in all long distances, the key is to keep moving forward :)Yay!.finished the article!.