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:
Courses:
- Popular Andrew Ng Introductory to Machine Learning Course
- Stanford Database
- Stanford Algorithms and Data Structure
- Coursera
- Geoffery Hinton Course
- Stanford CS231n
- Stanford CS224NLP
- Fast.ai
- MIT Linear Algebra
- Edx Microsoft Data Science Track
- Edx Spark Course
- Coursera Bayesian Network
- IBM Introduction to Data Science
- Stephen Boyd Optimization
- CS20si
Books:
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
Organizations:
- Data Science Nigeria
- AI Saturday