Data science and machine learning with Python in Senegal

This course is run on demand: in a virtual classroom or at your premises, on dates that suit you. Ask us for the next dates or a quote for your team.

• Prepare and clean data with pandas
• Explore and visualise data
• Build regression and classification models
• Evaluate and improve a model
• Present results and prepare a model for production

• Analysts, management controllers, research officers
• Developers moving into data
• Engineers and scientists

Basic Python (variables, loops, functions) and descriptive statistics. The "Python fundamentals" course is recommended.

1. Day 1

   Preparing data

   • –Python environment and notebooks
   • –pandas: loading, filtering, transforming
   • –Missing values and outliers

2. Day 2

   Exploring and visualising

   • –Descriptive statistics
   • –Visualisation with Matplotlib and Seaborn
   • –Formulating hypotheses

3. Day 3

   Supervised learning

   • –Linear and logistic regression
   • –Decision trees and random forests
   • –Train / test split

4. Day 4

   Evaluating and improving

   • –Metrics: precision, recall, AUC, error
   • –Cross-validation and hyperparameter tuning
   • –Unsupervised learning: clustering

5. Day 5

   End-to-end project

   • –Project on a real case
   • –Interpreting and explaining a model
   • –Reporting and moving to production

Guided notebooks and a running project on real data. Groups of up to 10 participants, in a virtual classroom or at your premises, with a practitioner trainer. For 30 days after the course, you can email your questions to the trainer.

• Annotated Python notebooks for every exercise
• pandas and scikit-learn cheat sheets
• Data science project methodology

Initial assessment, hands-on exercises and a final quiz. Certificate of attendance; Onarcle certificate of achievement from a 70% score on the final quiz.