Menu

Data Science Winter School

Foundations of Data Science with Python and R

Did you miss the winter session? Study this topic online or in Barcelona during our Data Science Summer School!

Summer School courses in Data Science

Course overview

This is an intensive 20-hour course based on a hands-on approach using Jupyter notebooks, all material is motivated by specific information retrieval and data analysis questions and each thematic unit concludes with a small project. The course provides basic training in data analysis and machine learning with Python and R.

Course director

facultyJoan Verdú
PhD, Universitat de Lleida
Head of Knowledge Transfer, Barcelona GSE Data Science Center

 

Key benefits

Even with little programming background, this course will give you basic tools to get started with data science, from data exploration to building your predictive models and extracting insights. Main programming language will be Python, with a final touch of data science with R coding.

Study with us this summer!

This topic is available as part of the Barcelona GSE Data Science Summer School:

  • Online edition: June 21-25, 2021
  • Face-to-face: July 5-9, 2021

Apply for Summer 2021

Email us for information about other upcoming courses.

Schedule

All times are Barcelona local time (CET).

Day 1

Time Session Keywords
9:30 - 9:45 Welcome and introduction  
9:45 - 10:50 Programming with Python data types, functions, objects
11:00 - 12:00 Data analysis with Python pandas
12:15 - 13:40 Data visualization with Python seaborn, scatterplot, lineplot, heatmap
13:45 - 14:00 Project assignment  
  Offline work on project assignments  
18:00 - 19:00 Online session with TA about project assignment and other doubts  

Day 2

Time Session Keywords
9:30 - 10:50 Linear models for regression sklearn, linear models, cross validation, regularisation, lasso
11:00 - 12:00 Linear models for classification sklearn, logistic regression, cross validation, regularisation, lasso
12:15 - 13:40 Non-linear models: Decision tree models sklearn, cross validation, regularisation, trees, ensembles, boosting
13:45 - 14:00 Project assignment  
  Offline work in project assignment  
18:00 - 19:00 Online session with TA about project assignment and other doubts  

Day 3

Time Session Keywords
9:30 - 9:45 Feedback from day 1 and day 2 assignments  
9:45 - 10:50 Data preparation handling missing data, encoding categorical data, normalizing data
11:00 - 12:00 Unsupervised learning (I) clustering, factors, independent component analysis and matrix factorization
12:15 - 13:40 Unsupervised learning (II)  
13:45 - 14:00 Project assignment  
  Offline work in project assignment  
18:00 - 19:00 Online session with TA about project assignment and other doubts  

Day 4

Time Session  
9:30 - 10:50 R basic programming data types, functions, objects
11:00 - 12:00 Data exploration and visualization with R  
12:15 - 13:40 Sample data preparation, supervised and unsupervised learning with R  
13:45 - 14:00 Project assignment  
  Offline work in project assignment  
18:00 - 19:00 Online session with TA about project assignment and other doubts  

Participant profile

The course is addressed to:

  • Master and PhD students
  • Professionals
  • Researchers and academics 

Prerequisites

  • Although not mandatory, some knowledge of Python, Jupyter notebooks, and algebra is recommended.
  • Students must have their own laptop or desktop computer, and a good internet connection. T
  • The latest version of Anaconda with Python and R should be installed, following instructions to be sent. Check hardware requirements to run Anaconda
  • Notebooks might be run as well using Google Collaboratory. Further instructions will be sent prior to the start of the course.

Course methodology

Required activities

  • (Online) class participation is compulsory. During classes there will be small projects that students are expected to perform while in class.
  • Students will have access to videos corresponding to most of the topics, where the notebooks and materials are explained in advance. They are supposed to watch them in advance, so that we can speed up during online sessions, focus on Q&A and solve the short exercises that are embedded in the notebooks.
  • At the end of each module (4 of them, one per day) there will be a project assignment, to be done:
    • Individually, during the same day, with some support from TAs (Teaching Assistants).
    • Optionally, as an extension, during one month, students may:
      1. Keep on working on each project, with some interaction with the course professor; and/or
      2. Work on his own project (in a similar scope), previously validated by the Barcelona GSE, with some support from a TA. This option implies an extra fee to be discussed individually. Please email the organizers for more details.

Evaluation

  • Class attendance and submission of projects
  • Projects will be given at the last class of each module (one per day)
    • The individual project should be delivered the same day. Expect an effort of 2-4 h each one.
    • Optional: Students will be given 4 weeks to submit the extended version of projects.
    • The grade will be the average of the individual projects (there will be feedback for the extended versions, but no grade).
    • There might be some quizzes to recap materials at the end of each module, not graded.

Competencies

  • Construct a global vision of the situation of the problem based on knowledge of the synergies between advanced statistical methods, computing and business analysis to generate added value.
  • Modeling and predicting high-dimensional data with advanced statistical methods in the field of data science in order to improve strategic decision making.
  • Apply the knowledge of programming languages, computer programs and advanced services in the Cloud to solve the problems that are presented to the data scientist.
  • Solve the real problems that arise in the fields of study through the accurate analysis of the data.
  • Students know to communicate their conclusions and the knowledge and last reasons that sustain them to specialized and non-specialized publics in a clear and unambiguous way.

Learning outcomes

  • Elaborate and estimate probabilistic prediction models based on certain data.
  • Apply supervised and semi-supervised learning algorithms.
  • Predicting information needs based on decisions that must be made.

 

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 Barcelona GSE Data Science Winter School, it met all of my expectations. The course gave an excellent overview of the theory and methods in Data Science with the use of Python. Intensive lectures, practical examples, and three individual projects that we had to complete during the course 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 weekend 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.

Fees

Next edition fees TBA.

Category Early-bird Registration
and Payment		 Fee
Regular fee TBA TBA
Reduced fee

  • Master's and PhD Students
  • Alumni of Barcelona GSE Master's programs
  • Unemployed
 TBA TBA

NOTE: Early-bird registration fee will only be applied if payment is received on or before Dec 31. After Dec 31, regular fee will be applied.

Special discounts are not cumulative.

Confirmation

No participant will be given access to the course unless s/he is officially registered and has paid the full course fee before the deadline (subject to seat availability and notified by the Admissions Office accordingly). Therefore, a place for the course is guaranteed only upon full payment of the course fees.

If a sponsor (participant's employer or university) is covering the course fees, the participant must provide their details in the application and specify if an invoice issued in advance will be required to arrange the payment.

Upon successful confirmation of payment, an email will be sent to the participant acknowledging receipt of the course fees and, therefore, confirmation of course registration.

Cancelation

Policies for the next edition TBA.

All cancelations must be received in advance by email and before course materials have been distributed. Participants wishing to withdraw from the course will have their course fees totally or partially refunded according to the following policies:

  • Until December 31, 2020: Full refund, except for 200€ to cover administration and admissions expenses.
  • Between January 1 and January 31, 2021: 50% of the course fee charged.
  • From February 1, 2021: No refund.

Once the course materials have been distributed, no requests for refunds will be accepted. All refunds will be processed after the course and confirmed by email.

Accommodation

This is an online course. Session streaming will be scheduled on Barcelona local time (CET).

Barcelona GSE Data Science Center: leading Data Science in Barcelona

Barcelona has become a hub for Analytics and Data Science within Europe, with many major companies establishing or relocating their Analytics teams in the city. The Barcelona Graduate School of Economics launched the Barcelona GSE Data Science Center in 2016 to boost a wide range of activities in this scientific area, consolidating its role as the academic point of reference related to this emerging field, by training many highly talented people, as well as promoting frontier research in Data Science.

In addition to the Winter School, the Center's education offer includes:

To learn more about these programs, as well a cutting-edge research and consulting services provided by the Center, visit the Barcelona GSE Data Science Center website.

© Barcelona Graduate School of Economics. All rights reserved.