Applied Data Analytics

This course covers essential techniques, methodologies, and practical skills for extracting meaningful insights from data. It is designed for doctoral students from various faculties at Université Paris Cité and provides a strong foundation in data science, analytics, machine learning, deep learning, and data mining.

Format of Lectures

This intensive course runs over one week in the fall, from Monday to Friday, between 10:00 AM and 1:00 PM, totalling approximately 15 hours.

Instruction includes slides, hands-on Google Colaboratory exercises, and questionnaires.

On the final day, students will present analysis challenges related to their theses.

The course is conducted via Zoom.

Information

The last session took place from November 18-22, 2024. The next session will be held in October 2025.

Interested students can enrol through https://u-paris.fr/doctorat/applied-data-analytics/.

Enrolled students who need access to course materials should contact me via email.

Learning Outcomes

By the end of this course, students will:

• Understand the basics of data preparation and supervised learning.

• Gain proficiency in regression and classification techniques.

• Explore advanced topics such as neural networks and deep learning.

• Apply unsupervised learning methods and generative models.

• Present and discuss their data analysis challenges and link them to the topics of the course.

Instruction Methods

• Lectures via Zoom using slides

• Practical exercises with Google Colaboratory

• Interactive polls and Q&A sessions

Assessment

• Participation in class discussions and polls

• Presentation of data analysis challenges on the final day

Course Material

The Zoom codes to view the recordings are available in the Introduction Lecture.
Enrolled students who need access to course materials should contact me via email.

• Lecture 1: Introduction
  Zoom recording 1

• Lecture 2: Data Preparation
  Zoom recording 2

• Lecture 3: Classic Supervised Learning: Machine Learning Algorithms, Boosted Decision Trees, Multi Layer Perceptrons
  Zoom recording 3

  • Colaboratory Notebooks:

    • Comparison of different classifiers

    • Binary Classification

    • Data-driven imputation for missing data

    • Techniques to handle imbalanced datasets

    • Classic Supervised Learning - Regression and Ensemble Learning

    • Multi-label Classification

    • Classification of written digits with an MLP

• Lecture 4: Deep Learning
  Zoom recording 4

  • Colaboratory Notebooks:

    • Exploring the MNIST dataset with a CNN

    • Image Classifier with the Cats and Dogs dataset

    • Time Series Forecasting with Recurrent Neural Networks

• Lecture 5: Unsupervised Learning
  Zoom recording 5

  • Colaboratory Notebooks:

    • Pseudo-labelling of tabular data through dimensionality reduction and clustering with UMAP and HDBSCAN