This is a 20-hour online course that exposes you to state-of-the-art tools employed in data science. The course is taught with a hands-on approach via Jupyter notebooks. The course is composed of three units that guide participants through the process of converting raw data into actionable insights: 

1. Data handling and visualization
2. Supervised learning: the course covers some of the most relevant supervised learning tools, ranging from linear models such as LASSO, Ridge and Elastic Net to nonlinear models, such as Decision Trees, Random Forests and Boosting
3. Unsupervised learning: participants are introduced to the main concepts and tools for dealing with unsupervised learning problems, such as clustering algorithms and Principal Components Analysis

Become familiar with the most important techniques used by data scientists

Classes will consist of rigorous training of the topics together with practical sessions to learn how to deploy these techniques to real data sets. Built around Jupyter Notebooks, this course offers participants the opportunity to improve their programming skills in both Python and R, the most widely used programming languages in data science.

After successful completion of this course, you will have: 

• Worked with and extracted valuable insights from real data

• Improved programming skills in two of the most used programming languages in data science
• Skills to understand some of the key methods, as well as their limitations, used by data scientists
• Gained practical experience in applying these methods to large and heterogeneous data
• Learnt how to work with large data sets

Get up to speed on the latest developments in data science in a short time

This course will be taught online but it will be live and interactive.