Data Science for Marketing
The Data Science for Marketing curricular unit aims at introducing the basic concepts of data exploration and knowledge mining to support decision-making with a data-driven mindset. During the semester, students will have the opportunity to learn techniques to assess the quality of data, prepare data for analysis, characterize and describe a dataset, implement linear and logistic regression models, 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.
Flávio Luís Portas Pinheiro
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
The curricular unit does not have technical enrollment requirements.
Past 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.
- VanderPlas, Jake. Python data science handbook: essential tools for working with data. "O'Reilly Media, Inc.," 2016.
- McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. "O'Reilly Media, Inc.," 2012.
- Grus, Joel. Data science from scratch: first principles with Python. "O'Reilly Media, Inc.," 2015
- 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.".
- Additional reading materials will be shared in Moodle with all the students, including documentation materials and book chapters;
The curricular unit is based on a mix between 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.
EE1 - Participation in classroom activities (50%)
EE2 - Practical Exam (50%).
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.
The first grading season is dedicated to continuous evaluation, which includes the following components:
- Classroom Activities (50%) ¿ During the semester students will be invited to participate in several classroom activities (e.g., assignments, quizzes, etc). The participation in the activities will be graded and contribute to the final mark. Grading and weights of each activity will be announced prior to the acitivity;
- Practical Exam (50%) ¿ The final practical challenge will take place during the last week of classes and consists of a 7-day assignment in which students need to implement the steps of a proposed data science project.
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.
The curricular unit is organized in three Learning Units (LU):
LU0. Introduction to Data Science
LU1. Use data mining techniques to extract knowledge from data.
LU2. Introduction to Supervised and Unsupervised data modelling.
LU3. Develop a data-driven analytical mindset to support your decisions.
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- Specialization in Knowledge Management and Business Intelligence
- Specialization in Information Systems and Technologies Management
- Specialization in Marketing Intelligence
- Specialization in Marketing Research and CRM
- Specialization in Knowledge Management and Business Intelligence – Working Hours Format
- Specialization in Information Systems and Technologies Management - Working Hours Format
- Specialization in Marketing Intelligence - Working Hours Format
- Post-Graduation in Information Analysis and Management
- Post-Graduation Risk Analysis and Management
- PostGraduate in Data Science for Marketing
- PostGraduate in Digital Enterprise Management
- PostGraduate Digital Marketing and Analytics
- PostGraduate in Information Management and Business Intelligence in Healthcare
- Post-Graduation in Knowledge Management and Business Intelligence
- Post-Graduation Information Systems and Technologies Management
- Post-Graduation in Marketing Intelligence
- Post-Graduation Marketing Research e CRM
- PostGraduate in Enterprise Information Systems