Languages

Started using Sololearn to learn Python in January 2017 on and off.

Started learning MATLAB in Summer 2018 - Coursera

Decided to Learn Python in December 2018 - Coursera and the py4e site

Courses and Materials

Did CS50 halfway

Dataquest

Datacamp for a stint

Finished Andrew Ng’s Machine Learning Course… Yay!! Definitely a good intro course to ML

Completed most of the courses for IBM Data Science Professional Certificate from Coursera’s syllabus in Cognitiveclass.ai for free. Completed exercises, got the badges and certificates as well

Syllabus is as follows:

  1. What is Data Science? (Introduction to Data Science in Cognitive class)
  2. Open Source tools for Data Science (Data Science Hands-On with Open Source Tools in Cognitive class)
  3. Data Science Methodology
  4. Python for Data Science and AI (Python for Data Science in Cognitive class)
  5. Databases and SQL for Data Science
  6. Data Analysis with Python
  7. Data Visualization with Python
  8. Machine Learning with Python
  9. Applied Data Science Capstone (Not in Cognitive class)

Databases and SQL for Data Science and Applied Data Science Capstone are the outstanding for me for now….

Using EliteDataScience Data Science Career Guide Roadmap of Topics:

  1. Understand the DS & ML workflow at a high level a. Read the ​Data Science Primer b. Read the guide to ​Modern Machine Learning Algorithms
  2. Learn Python programming basics a. Complete the ​Python for Data Science Quickstart Guide b. Bookmark this ​Python for DS Cheat Sheet
  3. Learn the basics of the Pandas library a. Complete the ​Python Data Wrangling Tutorial with Pandas b. Bookmark its ​official documentation page​ (you’ll reference it often)
  4. See the modeling process from start to finish a. Complete the ​Python Machine Learning Tutorial with Scikit-Learn b. Complete the ​Kaggle Titanic Dataset Training Competition
  5. Download more datasets you find interesting a. Download from a ​hand-picked list here​. b. Project ideas: ​Fun Machine Learning Projects for Beginners
  6. Practice the other core skills of applied ML using those datasets a. Data visualization and exploratory analysis (​Tutorial​) b. Data cleaning (​Examples​) c. Feature engineering (​Examples​, ​More Examples​)
  7. Build a portfolio of real-world projects. Then apply! a. See the question, ​“How do I build a portfolio of real-world projects?”

Using Daniel Bourke’s book recommendations …. Not read/perused all textbooks but gotten them…

  1. Learn Python the Hard Way by Zed Shaw
  2. Naked Statistics by Charles Wheelan
  3. Artificial Intelligence: A Modern Approach by Peter Norvig and Steven Russell
  4. Deep Learning with Python by François Chollet
  5. Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
  6. The Hundred-Page Machine Learning Book by Andriy Burkov