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

The Data Life Podcast

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Added six years ago

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We Have The Receipts

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Do you have fond childhood memories of summer camp? For a chance at $250,000, campers must compete in a series of summer camp-themed challenges to prove that they are unbeatable, unhateable, and unbreakable. Host Chris Burns is joined by the multi-talented comedian Dana Moon to recap the first five episodes of season one of Battle Camp . Plus, Quori-Tyler (aka QT) joins the podcast to dish on the camp gossip, team dynamics, and the Watson to her Sherlock Holmes. Leave us a voice message at www.speakpipe.com/WeHaveTheReceipts Text us at (929) 487-3621 DM Chris @FatCarrieBradshaw on Instagram Follow We Have The Receipts wherever you listen, so you never miss an episode. Listen to more from Netflix Podcasts.…

6 years ago 15:58

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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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