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Foundations of Data Science

Foundations of Data Science

Learn with our expert faculty

facultyChristian Brownlees
PhD, University of Florence
UPF and BSE
Director
facultyAndré B.M. Souza
PhD, UPF
Assistant Professor, ESADE
Instructor

Course overview

This is a 15-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

 

INTENSIVE COURSE

Foundations of Data Science

Applications will open soon!
  ONLINE FACE-TO-FACE
Regular Fee 1300 € 1475 €
Reduced Fee 775 € 850 €

10% early-bird discount applies to payments made on or before February 6, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: February 6, 2024

  ONLINE FACE-TO-FACE
Regular Fee 1300 € 1475 €
Reduced Fee 775 € 850 €

10% early-bird discount applies to payments made on or before February 6, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: March 3, 2024

  ONLINE FACE-TO-FACE
Regular Fee 1300 € 1475 €
Reduced Fee 775 € 850 €

10% early-bird discount applies to payments made on or before February 6, 2024 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Wednesday, March 6
Time Session
13:30 - 14:30 Registration
14:30 - 16:00 Missing Data and Visualization
Coffee Break
16:30 - 18:00 Linear Regression
Thursday, March 7
Time Session
09:30 - 11:00 Penalized Regression Models
Coffee Break
11:30 - 13:00 Classification
Lunch
14:30 - 16:00 k-Nearest Neighbors and CART
Coffee Break
16:30 - 18:00 Random Forests  + Boosting
Friday, March 8
Time Session
09:30 - 11:00  Clustering and PCA
Coffee Break
11:30 - 13:00 Intro to R
Lunch
14:30 - 16:00 Supervised Learning in R
Coffee Break
16:30 - 18:00 Unsupervised Learning in R

 

Course content

Read an outline of topics and bibliography for this course

Course outline

  1. Methodology: Introduction and Linear models
    Regression, Penalized Regression, LASSO, Ridge, Cross Validation, Least Squares.
  2. Practice: Linear models
  3. Methodology: Classification and introduction to flexible modeling
    Regression, Penalized Regression, LASSO, Ridge, Classification, AUC, ROC, Cross Validation, Least Squares,K-Nearest Neighbors, Decision Trees.
  4. Practice: Classification and introduction to flexible models
  5. Methodology: Supervised Learning with flexible models
    Nonlinear models, Random Forests, AdaBoost, Gradient Boosting.
  6. Practice: Supervised Learning
  7. Methodology: Unsupervised Learning
    Clustering, K-Means, Spectral Clustering, PCA, Factor Models.
  8. Practice: Unsupervised Learning
  9. Methodology: Data Science in R
  10. Practice: Data Science in R

References

  • The Elements of Statistical Learning. T. Hastie, R. Tibshirani, and J. Friedman. Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2001)

  • An Introduction to Statistical Learning: with Applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani, Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2021)
     

Prerequisites

Some knowledge of Python, R, Jupyter Notebooks, and matrix 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 on March 5 and 6, 2024.

Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course.

 

Testimonials

Konstantinos Pouliakas, Expert at CEDEFOP

As a researcher/professional of an EU agency, I was in need of a fast and succinct introduction to the principles of Machine Learning and Data Science. The course was very rigorous and covered targeted material and Python programs that may act as a springboard for more reading and learning on the topic...The course did a good job highlighting to potential Data Scientists that one needs rigorous theoretical understanding of the behavioral models, as opposed to simply programming some lines of code. I would highly recommend the course to professionals who, like myself, do not have the luxury of time due to family or other constraints to attend lengthy courses and lectures.

Liudmila Alekseeva, PhD Candidate in Finance, IESE

I enjoyed the BSE Data Science course. It met all of my expectations. I got an excellent overview of the theory and methods in Data Science with the use of Python. Intensive lectures, practical examples, and the individual projects we completed made the learning process very effective. I also appreciate that the professor not only gave us tools, but also explained the theory underlying them and the potential problems related to their implementation. After this course, I did not become a professional data scientist in Python at once, but the course gave me a fast start and a great motivation to continue developing in this field.

Participant Profile

This course is specifically aimed at

  1. Students: graduate students looking to build a strong foundation in machine learning.
  2. Professionals: professionals and consultants who want to transition into data science and machine learning roles and are interested in expanding their machine learning skills for predictive modeling and data-driven decision-making.
  3. Researchers: Researchers, academics and PhD students seeking to incorporate machine learning methods into their research projects

Fees

The fees for this course are listed below and all available discounts can be found here. 

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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