Applied Data Analytics

Résumé de la formation

Learning Outcomes

This course provides essential techniques, methodologies, and practical skills needed to extract meaningful insights from data. Throughout this course, you will learn basics skills in Machine Learning and Data Preparation.
It is designed for a diverse range of doctoral students from various faculties at the UPCité. Key topics covered in the course include:

  • Data Science
  • Data Analytics
  • Machine Learning
  • Deep Learning
  • Data Mining

Format of lectures

The course will take place over a dedicated week in the fall, running from Monday to Friday in the mornings, from 10:00 AM to 13:00 PM, totalling approximately 15 hours. Instruction will be delivered through a combination of slides, Google Colab exercises, and questionnaires. On the final day, students will have the opportunity to present the analysis challenges related to their theses.
The course will be conducted via Zoom.

 

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Programme

Program overview

  • The detailed program and syllabus can be access HERE

Monday

14/12/26

Tuesday

15/12/26

Wednesday 16/12/26

Thursday 17/12/26

Friday

18/12/26

10h00-11h20

Introduction
Objective of the course

10h00-11h20

Classic supervised Learning

10h00-11h20

Classic Supervised Learning

Regression

10h00-11h20

Neural Networks

10h00-11h20

Unsupervised Learning & Generative Models

11h20-11h30 Break and

Poll ☕

11h20-11h30 Break and

Poll ☕

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll  ☕

11h30-13h00

Data preparation

11h30-13h00

Classic supervised Classification

11h30-13h00

Notebooks

11h30-13h00

Deep Learning

 

11h30-13h00

Students present their data analysis challenges

Formateurs et formatrices

Educational contact: Yvonne Becherini

Informations pratiques

Practical Information

 

Dates : Starting on the 14th of December until the 18th December 2026
From 10 a.m to 1p.m

-→ This training takes place online via Zoom. The link will be send to you directly by the trainer.

 

Rules of Attendance

-→ This course takes 15 hours.

-→ Attendance is mandatory for at least 4 days of training.

You will get credited :

  • Attendance for 5 days: 15 hours of training
  • Attendance for 4 days: 12 hours of training
  • Attendance under 4 days: no hours of training will be credited

 

 

Doctorat, English speaking courses, Mathématiques et statistiques, Sciences des données

Public

Ph.D Students

Duration

5 days / 15 hours

Language

English

Format

Online

Code

DF26ONADA

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