The task of transitioning to a new field is challenging ! not for the faint hearted… It is not very different from climbing a mountain !
To become a data scientist you need to learn
- Some math (Stats, linear algebra, optimization)
- Programming (preferably Python / R)
- The art of working with and analyzing data
But this is a path people have tread before : There are many resources available to learn these skills… Take a look at the following video for a brief overview of the various kinds of roles in data science and transitioning into them.
Let us know actually look at some of the resources available to learn :
1. Free Resources
Yes – you heard it right free!
There are many resources available to learn for free.
- Python : There is plenty of material online. LearnPython is a great place to learn python for free! is a great place to take it to the next level and learn about ML libraries. You can pay a nominal monthly charge to access all their content.
- ML Basics – The ML course by Anderw Ng and the Deep Learning Specialization track are great on (you can audit these courses for free).
- Math and Stats : Khan academy has fantastic resources on math and stats. Lectures of Gilbert Strang on youtube are great for Linear Algebra. All absolutely free.
- The art of analyzing data and coming up with models and patterns is in my opinion something you master when you do lots and lots of projects. A good starter is Kaggle !!
One needs to be self motivated to be able to learn through these resources and transition to a data science career.
2. Structured Online Learning :
For professionals who value their time …
It is often hard to devote time and transition to a new field. So if you are a developer and are looking to gradually transition to data science, while working on your full-time job, and need more structure that can fit into your busy schedule, there are several online-courses. These are not free, but ensure you get value for your buck and transition to being a data scientist in a structured way.
- Springboard : Great course with 1-1 learning with top industry mentors for professionals who want to transition to data science with a job guarantee.
- AppliedAICourse: Does a good job at introducing concepts. Very popular with freshers and people in early career.
- LambdaSchool : Pay once you get a job
- Upgrad : Yet another data science course
Some of these courses, specially Springboard does an incredible job with mentor lead learning where you can learn 1–1 with top industry experts and do projects with them. Weekly calls ensure that you do not slack off ! Springboard even has a money-back guarantee program. Send e-mail to firstname.lastname@example.org for a discount code on Springboard fee !
3. Formal Data Science Education :
But there is a lot of knowledge underneath this tip to uncover ! One can do a formal Masters / PhD in ML to go deeper into the topic.
However, this involves a lot of financial planning specially if you want to do this full time. Even to do it part-time, requires significant commitment to manage a full fledged post-grad degree with your job. You can go this way if this is something you really really want to do.
Preparing for Interviews !!
What we specialize at MachineLearningInterview.com is to help you nail your interview – to bring out the best in what you already know – whatever path you choose to learn the basics !
You need support during that last mile – do you have the right profile (linkedin / resume / github profile), are you able to articulate your answers, do you get the concepts and express them well ? Join us – and we want to get you the best you can from an interview !!
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