Foundations of Data Science, March 20 to 31

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

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

Some knowledge of Python, R, Jupyter Notebooks, and algebra is recommended to fully benefit from the course, which will be run in Google Collaboratory.

Those with limited or no experience in either R or Python must register separately for the course Introduction to Python and R Programming for Data Science, which will be conducted online from March 14 to 17, 2023.

This course is specifically aimed at: 

Professionals, researchers, and Master's/PhD students who want to learn machine learning tools to get insights from large collections of data for their business or research projects.

In particular, this course may also be of interest to:

• Master's graduates in economics, finance or a similar field who obtained their degree more than 5 years ago and are interested in learning the latest developments and trends in the field of data science.

• Managers of a team of data scientists and whose role consists of overviewing and directing the efforts of the team.

You can find the full course outline and more details on the course page