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UE Data analytics for industrial engineering - WGUS2092

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  • Number of hours

    • Lectures : 30.0
    • Tutorials : -
    • Laboratory works : 24.0
    • Projects : -
    • Internship : -
    • Written tests : 1.0
    ECTS : 3.0

Goals

Part I
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.

Part II
The course introduces the first tools for processing quantitative and qualitative data using machine learning.
The analysis methods that will be learned allow for automatic classification; construction of predictive models; evaluation of the performance of the methods; diagnosis of the limits of the applications of these methods.

Content

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

Part II
.1 Issues in machine learning, supervised machine learning (regression, classification)
supervised vs unsupervised methods (quick presentation of some unsupervised methods (k-means, dendrograms)).
.2 Regression and classification methods: linear regression and logistic regression; models, algorithms and resolutions
.3 Internal evaluation of regression and classification: Errors, residuals and prediction evaluation
.4 External Evaluation: Statistical Assumptions and Model Evaluation
.5 Implementation on different databases

Prerequisites

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

Tests

This weighting is compatible with the organization of distance learning courses and exams

Part I
Continuous assessment grade : TP (based on at least 2 grades TP1 and TP2)
Individual examination grade : EX
Grade = 0.4*TP + 0.6*EX
Part II
Continuous assessment grade : TP (based on at least 2 grades TP1 and TP2)
Individual examination grade : EX
Grade = 0.4*TP + 0.6*EX

Cette pondération est compatible avec une organisation des enseignements et des examens en distanciel

Notes de contrôle continu (au moins 2 notes de TP: TP1 et TP2)
Une note d'examen individuelle: E1

Note = 0.4*((TP1+TP2)/2) + 0.6*E1

Calendar

The course exists in the following branches:

  • Curriculum - Master 1 GI SIE program - Semester 7
see the course schedule for 2022-2023

Additional Information

Course ID : WGUS2092
Course language(s): FR

You can find this course among all other courses.

Bibliography

Elff, (2020), Data Management in R , SAGE publication
Nicholas J. Horton and Ken Kleinman , (2016), Using R and RStudio for Data Management, Statistical Analysis, and Graphics (second edition)

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Date of update June 20, 2022

Université Grenoble Alpes