Note: Work in progress. This webpage is currently still under development. Courses may be incomplete.
  • Python data types
  • Strings, integers, floats, bools
  • Python data structures
  • Lists, dictionaries, sets, tuples
  • Mutable vs immutable types
  • Control Flow
  • List comprehension
  • Python functions
  • Namespace and scope
  • Global vs local variables

Introduction to Python

Basics of the Python language

Python is a high-level and general-purpose computer language that is useful for many applications such as machine learning, website backend development, software testing and prototyping, scientific computing or simply to automate everyday tasks. This course is concerned with equipping the reader with the basics of the Python programming language. The goal is to offer a comprehensive guide to the nuts and bolts of the language to prepare the reader for more advanced topics on data analysis and machine learning in subsequent courses.

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NumPy Arrays

NumPy Arrays for scientific computing

NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It offers comprehensive mathematical functions, random number generators, linear algebra and Fourier transforms routines, etc. In this course, we learn the basics of the NumPy array object and provide a few examples of its use in scientific computing.

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  • Python data types
  • Strings, integers, floats, bools
  • Python data structures
  • Lists, dictionaries, sets, tuples
  • Mutable vs immutable types
  • Control Flow
  • List comprehension
  • Python functions
  • Namespace and scope
  • Global vs local variables

Introduction to pandas Dataframes

Data Manipulation with pandas

pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating tabular data and time series. In this course, we will learn how to use pandas to import data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. Also, we will learn about data manipulation operations such as merging, reshaping, filtering, selecting, etc. and other data wrangling methods.

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