Ingénierie Logicielle pour la Science des Données

1. Algorithmique et structures de données 1 et 2

2. POO

3. Introduction au GL, Analyse et conception des systèmes d’information

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you’ll learn all about the important ideas of modularity, documentation, data analysis & automated ML deployment, and you’ll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You’ll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

Why should you as a Data Scientist care about Software Engineering concepts? Here we’ll cover specific Software Engineering concepts and how these important ideas can revolutionize your Data Science workflow!

1. Learn to use Pandas, NumP, SciPy, etc for Data Analysis and Numerical Data

2. Learn to use Matplotlib and Seaborn for statistical plots

3. Learn to use Falsk for web application development and ML deployment

4. Ect

1. Introduction to Jupyter Notebook & Data Types (integer, float, boolean, string, etc)
2. Variables, Lists, Tuples and Dictionaries
3. IF Statement and FOR loop
4. Functions & Modules
5. Object Orientated Programming and Classes

1. Data Processing with Pandas
2. Data Processing with Numpy
3. Data manipulation with SciPy

1. Data Visualization with Matplotlib
2. Data Visualization with Seaborn

1. Full MVC application with flask
2. Building RESTful Web Service with Flask

Consultez les ressources disponibles concernant ce module sur le moteur de recherche de la bibliothèque, ou accédez directement au cours de vos enseignants via la plateforme de téléenseignement de l’école « e-learn ».