Here you have the main topics and for each one there are some suggested courses that you should take one or more of them.
> Notice that any roadmap is not sacred, you may find some courses better than the suggested ones from your perspective and that's completely okay, however, you should go through all the topics.
Python3 Specialization
Python for Everybody specialization
Intro to cs with python MIT
- Pandas and Numpy
Google first course(data, data)
Pandas (Corey playlist)
Pandas and numpy (Udacity) - Cleaning
Cleaning blog
Cleaning session
Cleaning datacamp
Cleaning (part of google specialization) - EDA
Ask questions(from google specialization)
Visualizing intro to seaborn (datacamp)
Visualizing intro to matplotlib (datacamp)
Visualizing intermediate seaborn (datacamp)
EDA (datacamp)
EDA (from google specialization) - Dashboards
1. Tableau:
Tableau (from google specialization)
Tableau (Udacity)
Tableau learning
2. Power BI:
Power BI (Coursera)
Power BI learning - More Statistics
Inferential stats (Udacity)
Think stats (book)
Intro to sql (datacamp)
Intro to sql and db (datacamp)
Intermediate sql (datacamp)
Git (Udacity)
Intro to web scraping (datacamp)
Web scraping (datacamp)
- Probability and linear algebra
Probability (Khanacademy)
Probability MIT playlist
Linear algebra for ML Coursera specialization - Machine learning
Machine learning andrew
Machine learning IBM
Hands on ML 2nd edition book
Videos of Hands on ML 2nd edition
Feature engineering book
Deep learning specialization Andrew
Deep learning book