X-Datascience Datacamp

Datacamp class for master student - 5 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 day you will have 50% of lectures and 50% of work on the competitive challenge using the RAMP website.

Instructors:

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

Location

The course will be during the week from Jan 9 to Jan 13 in person.

To join the discord channel use this URL

On GitHub you have some teaching materials at: https://github.com/x-datascience-datacamp

You must have a GitHub account to complete the course.

Day 1: Data wrangling

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

Day 2: ML Pipelines and model evaluation

• Advanced scikit-learn: Column transformer and pipelines
• Parallel processing with joblib
• Generalization and Cross Validation
• Assignment sklearn
• Getting started on RAMP & Introduction to the challenges.

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
• Working on data challenges

Day 4: Feature engineering and model inspection

• Feature engineering and advanced encoding of categorical features
• Model inspection: Partial dependence plots, Feature importance
• Working on data challenges

Day 5: Ensemble methods and hyperparameter optimization

• From trees to gradient boosting
• Profiling with snakeviz
• Hyperparameter optimization
• Working on data challenges