Where to start? How to proceed?

I’ve been toying with all of this for about a month now. I have so many bookmarks for blogs, podcasts, free courses, paid courses, and on and on. I’ve checked out books from the library, bought others on Amazon, and downloaded open source texts. I have to admit that I’m a bit daunted. There are a million places to begin, and I have plenty of work to do before even becoming mildly employable. We’ll see…

Here’s my game plan:

  • Books to read
    • Data Science from Scratch by Joel Grus
    • Python for Data Analysis by Wes McKinney
    • Hands on Machine Learning by Aurélien Géron
    • The Art of Data Science by Elizabeth Matsui and Roger Peng
    • OpenIntro to Statistics by David Diez et. al.
  • Paid Online Courses
    • The entire Data Scientist Career Track from DataCamp.
    • Udemy
      • Python Megacourse
      • Python for Data Analysis & Visualization
      • Python for Machine Learning
      • Deep Learning x 4
  • Free Online Courses
    • Udacity
      • Intro to Computer Science
      • Intro to Data Science
    • Stanford
      • Statistical Inference
      • Prob – Stats
      • Statistical Learning (certified)
      • Mining Massive Datasets (certified)
      • Algorithms 1 & 2 (certified)

This should all take me the better part of a year. I hope to get enough dirt under my nails to start some simple projects soon, which will be posted here for all your viewing pleasure.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: