Notes de cours programming for data science

A Programming for Data Science course is designed to teach the fundamental programming and data analysis skills needed to solve complex problems in a variety of fields. Here is a typical description of the aspects covered in such a course: Course objectives: Understand the basics of programming with common languages used in data science, such as Python or R. Acquire practical skills in data manipulation, visualization, and analysis. Learn fundamental data science concepts, including data management, algorithms, and task automation. Develop analytical thinking and problem solving skills. A Programming for Data Science course is designed to teach the fundamental programming and data analysis skills needed to solve complex problems in a variety of fields. Here is a typical description of the aspects covered in such a course: Course objectives: Understand the basics of programming with common languages used in data science, such as Python or R. Acquire practical skills in data manipulation, visualization, and analysis. Learn fundamental data science concepts, including data management, algorithms, and task automation. Develop analytical thinking and problem solving skills. Course structure and content: 1. Introduction to programming: Basic concepts: variables, data types, loops, and conditions. Introduction to languages like Python or R. Use of development environments (Jupyter Notebook, RStudio, etc.). 2. Data manipulation: Loading and manipulating files (CSV, Excel, JSON). Introduction to data manipulation libraries like Pandas (Python) or dplyr (R). Data cleaning and management of missing values. 3. Data visualization: Create charts with tools like Matplotlib, Seaborn (Python), or ggplot2 (R). Customizing visualizations for better communication of results. 4. Basics of statistics and probability: Basic statistical concepts: mean, median, variance, etc. Introduction to probability distributions and their application in data analysis. 5. Introduction to databases: Basic concepts of relational databases. SQL language for querying and manipulating databases. 6. Automation and data pipelines: Scripts to automate repetitive tasks. Use of libraries for managing workflows like Airflow or Luigi. 7. Introduction to machine learning (optional): Fundamental concepts of machine learning. Use of tools like Scikit-Learn for simple models (regression, classification). 8. Final project: Application of the skills learned to a real project. Cleaning, analysis, visualization, and interpretation of real data. Presentation of results in the form of a report or interactive dashboard. Skills developed: Data-oriented programming. Manipulation and transformation of complex data. Visualization and communication of results. Analytical thinking and decision making based on data.