Data Engineering techniics and responsibilities - 4GMC1411

Goal(s)

Students will learn about data preparation methods for machine learning, knowledge engineering and text mining, and how to integrate them into data science projects.
Students will know how to manage their data, sort it, and organize it efficiently. They will be able to present relevant visualizations of their data and results. They will have acquired the behavior of a responsible and ethical data engineer.

Content(s)

B0 Introduction: Data Science Project Management
Steering data science projects, based on CRISP-DM
B1 Data handling & Data Engineer responsibilities (ethics, security, etc.)
B1.1 Technical data management
Data format, variable formats; basic operations (reads, writes; sorts; selections, projections, filters; merges)
B1.2 Technical management of results (visualization)
Types of graphics, principles of good visualization
Make and discuss technical choices and representations
B1.3 Societal management
Legal aspects (RGPD), sustainable aspects (risks on people [customer and staff] as well as environmental costs), security (who holds the data, spying...).
B1.4 Implementation : Micro-project

Students will have taken and validated the following courses: Probability and Statistics; Programming with R