6 Steps to Transition to Data Science from non-CS background
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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