In this episode we will talk all about the various steps to transition to data science from non computer science backgrounds.
One of the main difficulties people face from non-CS backgrounds is how overwhelming it can be to transition to data science field, I talk about my own journey, and share the 6 steps which can help you in your own data science career! 

00:00 to 02:10: Introduction

02:11 to 06:00: My Background of moving to data science from electrical engineering

06:01 to 10:56: Steps 1 to 3 covering things like using external APIs, already processed datasets and performing full stack data science work

10:57 to 11:55: Break sponsored by Anchor

11:56: End: Steps 4 to 6 covering things like math and statistics, machine learning pipelines and data structures & algorithms

Some useful links:

1) Andrew Ng Deep Learning Specialization Coursera https://www.coursera.org/specializations/deep-learning

2) Intro to Statistics by Sebastien Thrun https://www.udacity.com/course/intro-to-statistics--st101

3) Aurelion Geron's book on machine learning https://www.amazon.com/dp/1491962291/?tag=omnilence-20

4) Pramp for mock algorithm sessions on video https://www.pramp.com/ 

5) Leetcode for algorithm question datasets https://leetcode.com/ 

Some great datasets to get started in machine learning: 

6) MNIST for hand written digits https://www.kaggle.com/c/digit-recognizer

7) Iris dataset for flower classification http://archive.ics.uci.edu/ml/datasets/iris

8) IMDB movie reviews https://ai.stanford.edu/~amaas/data/sentiment/

Thanks for listening! 

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