My Data Science Journey

Welcome once again, kindly follow me on a brief trip where I highlight some resources that helped me become a Data Scientist having graduated with Bachelor of Pharamcy.

I have wanted to write codes when in my early undergraduate days, but the load of my undergraduate coursework couldn’t allow me finish up well to the level I wanted, I had to ditch writing codes at a time. However, as things will pan out, after my undergraduate studies, I got exposed to MOOCs, which I believe is the best thing that has ever happened to humanity - the ability to learn anything from anywhere.

Here are the list of Resources (MOOCs and books) that helped me land my first Internship as a Data Scientist without Computer Science Degree background:


  • Popular Andrew Ng Introductory to Machine Learning Course
  • Stanford Database
  • Stanford Algorithms and Data Structure
  • Coursera
  • Geoffery Hinton Course
  • Stanford CS231n
  • Stanford CS224NLP
  • MIT Linear Algebra
  • Edx Microsoft Data Science Track
  • Edx Spark Course
  • Coursera Bayesian Network
  • IBM Introduction to Data Science
  • Stephen Boyd Optimization
  • CS20si


Here are the books I found to be most useful in helping me solidify my Data Science and Deep Learning Expertise.

Data Science and Machine Learning

  • Sebastian Rachka
  • Aurelio Geron
  • Francois Chollet
  • Ian GoodFellow
  • Machine Learning with Tensorflow - Nishan Shukla
  • Stephen Boyd Optimization Book

Spark and Scala

  • Dean Wampler - Programming Scala
    • A good reference book for getting footings with programming scala
  • Learnaing Spark - Holden Karau, Andy Konwinski
  • High Performance Spark - Holden Karau
  • Kafka - The Definitive Guide - Neha Narkhede
  • LightBends Deploying machine learning model
  • Martin Ordesky
  • Tom White - Hadoop the Definitive Guide


  • Data Science Nigeria
  • AI Saturday