Books and MOOCs and projects simultaneously.
Medium: read current articles related to each topic ( the point here is that since articles are
more recent than book you will have state of the art knowledge)
Data analysis:
● Python: Python for data analysis book (premier projet à partir du chapitre 7)
● BI tools (Tableau): Data Visualization and Communication with Tableau (Coursera)
● SQL: datacamp (Introduction to SQL, Joining data in SQL, Intermediate SQL,
Introduction to relational Databases in SQL, Exploratory Data Analysis in SQL,
Database Design, Data Driven decision making in SQL) + Projects
● Other projects:
https://careerfoundry.com/en/blog/data-analytics/data-analytics-portfolio-project-ideas
/ 3&4; EDA of Netflix Contents | Kaggle;
Modeling:
● ML models: Machine Learning For Absolute Beginners: A Plain English Introduction
(2nd Edition)
●
● DL models: Deep Learning with Python - Book
● Explainability: Interpretable machine learning - Book
● Projects:
https://analyticsindiamag.com/machine-learning-101-ten-projects-for-high-school-stu
dents-to-get-started/;
https://www.kdnuggets.com/2021/09/20-machine-learning-projects-hired.html;
Data communication:
● Storytelling & Dashboarding: Storytelling with Data: A Data Visualization Guide for
Business Professionals - Book
● Projects: https://www.projectpro.io/article/-tableau-projects-ideas/479
(From here project will be normal projects we can find anywhere, using the three precedent
bricks and adding the following ones)
Project management:
● Managing a DS project: Managing Data Science: Effective strategies to manage data
science projects and build a sustainable team (kindle) Production:
● Introduction to production Coursera
● Airflow: orchestration: coursera
● Data engineering: coursera
ML Platform:
● GCP - Coursera
● AWS - Getting Started with AWS Machine Learning ( Coursera)
Advanced python :
● Python Data Science Toolbox (Part 1)
● Python Data Science Toolbox (Part 2)
https://elitedatascience.com/data-science-resources#foundational-skills