X-Datascience Datacamp

Datacamp class for master student - 4 days

The aim of this course is to learn data science by doing. All aspects of completing a data science pipeline will be covered, from exploratory data analysis (EDA), feature engineering, parameter optimization to advanced learning algorithms. You will also need to setup your own challenge!

Grade is a mix of your performance on the data challenge offered to the class as well as the challenge you will setup.

Each you will 50% of lectures and 50% of work on the competitive challenge using the RAMP website.

Instructors:

• Alexandre Gramfort ([email protected])
• Thomas Moreau ([email protected])

Day 1: Data wrangling

• Introduction to the workflow (VSCode, git, github, tests, ...)
• Advanced course on Pandas
• Github assignments: numpy and pandas

Day 2: ML Pipelines and model evaluation

• Advanced scikit-learn: Column transformer and pipelines
• Generalization and Cross Validation
• Getting started on RAMP: Challenge.0 - Brevet des colleges

Day 3: Metrics and dealing with unbalanced data

• Presentation of the different ML metrics
• Problem of the metric with imbalanced data
• ML approaches to deal with imbalanced data
• Introduction of the challenges:
  • Challenge.1: MEG source localisation (challenge)
  • Challenge.2: Step detection in human locomotion (challenge)
  • Challenge.3: Variable Stars (challenge)

Day 4: Ensemble methods and feature engineering

• Feature engineering and dealing with categorical features
• Model inspection: Partial dependence plots, Feature importance, SHAP