My DIY Masters in Data Science and AI

Image by Seanbatty from Pixabay

I decided to create my own Masters Program. It’s partly because I’m disillusioned by the outrageous costs of university tuition and formal degrees. Sure, there were some great classes I took as an undergrad, but even in that environment the majority of my learning came from outside the classroom (often accompanied by a pack of IPAs or a joint).

As an entrepreneur, self-learning is pretty much in the job description. I completed Y Combinator’s Startup School program a couple months ago and felt my ability to grow my startup was limited as a solo, non-technical founder. So I decided to create this “Masters” degree to maintain positive momentum while filling gaps in my knowledge.

I opted for an emphasis on application rather than theory. The master practitioner has a deep intuition for his craft. I chose specific course sequences that would teach me practical skills and let me build out a portfolio of projects. I also intentionally selected courses from a variety of institutions to learn from a range of perspectives.

One quick note on the Coursera specializations… I felt that paying to complete them would help motivate me. It’s definitely not mandatory as the courses can be taken for free, but it created a layer of legitimacy and sense of urgency in my mind.

Without further ado, here’s my Masters in Data Science and Artificial Intelligence:

Coursework

• Applied Data Science with Python Specialization (University of Michigan, Coursera)
  • Introduction to Data Science in Python
  • Applied Plotting, Charting & Data Representation in Python
  • Applied Machine Learning in Python
  • Applied Text Mining in Python
  • Applied Social Network Analysis in Python
  • Cost: $49/month
• TensorFlow in Practice Specialization (deeplearning.ai, Coursera)
  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  • Convolutional Neural Networks in TensorFlow
  • Natural Language Processing in TensorFlow
  • Sequences, Time Series and Prediction
  • Cost: $49/month
• Machine Learning with TensorFlow on Google Cloud Platform Specialization (Google, Coursera)
  • How Google does Machine Learning
  • Launching into Machine Learning
  • Intro to TensorFlow
  • Feature Engineering
  • Art and Science of Machine Learning
  • Cost: $49/month
• Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization (Google, Coursera)
  • End-to-End Machine Learning with TensorFlow on GCP
  • Production Machine Learning Systems
  • Image Understanding with TensorFlow on GCP
  • Sequence Models for Time Series and Natural Language Processing
  • Recommendation Systems with TensorFlow on GCP
  • Cost: $49/month
• Miscellaneous (Electives, I guess…?)
  • Databases and SQL for Data Science (IBM, Coursera)
  • 2 Health-related courses
    • Introduction to Food and Health (Stanford, Coursera)
    • TBD
  • Cost: Free!

Practicals

• Independent Project – more to come later but will be related to Chess960 (otherwise known as Fischer Random)
• Kaggle Competitions
  • 2 bronze medals OR
  • 1 silver medal
• Communication
  • Early write-up
  • Post write-up

Assuming I finish each Specialization in less than a month, that brings the total cost to just $200, or about 141 McChickens with tax. I’m optimistic the price will be well worth the knowledge and skills gained. For comparison, finishing this program will cost about 1/20th the price of a single university course for credit (~$4000).

Questions on how I designed the program? Have you created an independent learning experience for yourself? Leave a comment with your thoughts below!

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