Data Science I


The Data Science for Hospitality and Tourism I (Descriptive Analytics) curricular unit aims at introducing the basic concepts of data exploration and knowledge mining to support advanced data analytics and decision-making. During the semester, students will be introduced to Python and Jupyter Notebook as a working environment. We will explore techniques to assess the quality of data, prepare data for analysis, characterize, and describe a dataset, use clustering techniques, and network analysis for client/product segmentation. By the end of the semester, students will be equipped with the skills and toolset to develop a data-driven descriptive analysis independently to extract useful and relevant knowledge to support business decisions.


Practical activities will be developed in Python programming language. We will make use of the widely popular and useful libraries available (Pandas, Numpy, Scipy, NetworkX, Scikit Learn, Matplotlib, Seaborn, and statsmodel) that Python, the favorite framework among data scientists. The curricular unit has a strong active learning component. Hence, we expect students to participate during class activities and read the recommended weekly materials beforehand.

General characterization





Responsible teacher

Flávio Luís Portas Pinheiro


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


The curricular unit does not have technical enrollment requirements.
Experience with programming is recommended but not required.
Classes will be delivered in English. As such students are expected to have a good level of comprehension and communication in English.


  1. VanderPlas, Jake. Python data science handbook: essential tools for working with data. "O'Reilly Media, Inc.," 2016.
  2. McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. "O'Reilly Media, Inc.," 2012.
  3. Grus, Joel. Data science from scratch: first principles with Python. "O'Reilly Media, Inc.," 2015
  4. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
  5. Additional reading materials will be shared in Moodle with all the students, including documentation materials and book chapters; 

Teaching method

The curricular unit is based on a mix of theoretical and practical lessons with a strong, active learning component. During each session, students are exposed to new concepts and methodologies, case studies, and the resolution of examples. Active learning activities (debates, quizzes, mud cards, compare-and-contrast, homework assignments) will foster students¿ participation in the classroom, promoting peer-teaching and incite discussion. 

Evaluation Elements:
EE1 - Participation in classroom activities (40%)
EE2 - Practical Exam (60%).

Evaluation method

To successfully finish this curricular unit, students need to score a minimum of 9.5 points. The grading is divided into two seasons. Attendance in the second is optional for students that passed the curricular unit in the first season and can be used to improve their grade. 

First Season

The first grading season is dedicated to continuous evaluation, which includes the following components:

  • Classroom Activities (40%) ¿ During the semester (weeks 3, 5, 7, and 10) the students will be presented with a series of short problem sets to solve at home. In the following Lab, students will present their results in a 2-slide power-point presentation, which should also be submitted through Moodle. Grading will be based on participation and the quality of the presented solution. This is an individual assessment activity, which aims at allowing students to test their learning progress and promote discussion in classes;
  • Final Project (60%) ¿ This is a group activity. Groups of up to three students will be asked to develop a data descriptive project on a topic of their choice in the field of Hospitality and Tourism. Deliveries include a 5-page report and a final 15-minute presentation to the entire class plus 5 minutes of open discussion. Students will be evaluated by their ability to use the descriptive methods learned in the classes to construct a data-driven story. The delivery deadline will be decided with the class in the first class.

Second Season

The second grading season will take place in January and consists of a multiple-choice exam. The Exam consists of 40 questions. Correct answers count 0.5 points, and incorrect answers discount 0.2 points.

Subject matter

The curricular unit is organized into three Learning Units (LU):
LU0. Introduction to Data Science
LU1. Use data mining techniques to prepare data and extract knowledge.
LU2. Introduction Unsupervised Learning and data modeling.
LU3. Develop a data-driven analytical mindset to support your decisions.