Python for Everybody - Specialization
The course provides a comprehensive introduction to Python programming, one of the most popular programming languages today. The curriculum progresses from foundational concepts such as variables, functions, and conditional statements to more advanced topics, including database integration and API interactions. The instructor is the renowed professor Dr. Charles Severance, and the full content is available for free in his website.
With Coursera and University of Michigan we can also receive a certificate upon successful completion. The course is divided in 5 modules, as follows:
✅ Programming for Everybody (Getting Started with Python): explore the basics of python programming language, like synthax and semantics, conditional execution, functions, and iteration.
✅ Python Data Structures: explain the principles of data structures & how they are used. Understand strings, tuples, files, lists, and dictionaries in python.
✅ Using Python to Access Web Data: using RegEx to extract data from strings. Retrieve data from websites and APIs using python and work with XML & Json files.
✅ Using Databases with Python: use CRUD (create, read, update, delete) operations to interact with databases. Learn about Object-Oriented Programming in python.
✅ Capstone: Retrieving, Processing, and Visualizing Data with Python: creating real-wolrd projects in python.
All the exercises and projects that I’ve developed during the course can be found in in this Github Repository 
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My certificate \o/ for this course can be found here 👩🏻🎓
Data Analytics with Python
The course offered by the University of São Paulo teaches how to use Python for Data Analysis and Data Science. It explores key machine learning concepts, including unsupervised techniques (clustering, correspondence analysis, factor analysis, and PCA) and supervised techniques (multiple and simple regression). The main goal is to learn how to apply these techniques to analyze data and extract meaningful insights using popular Python libraries such as Pandas, Matplotlib, NumPy, and Seaborn.