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Data Science for Marketing

Objectives

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.

General characterization

Code

200201

Credits

7.5

Responsible teacher

Flávio Luís Portas Pinheiro

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

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.

Bibliography

  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 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. 

Evaluation Elements:

EE1 - Participation in classroom activities (50%)

EE2 - Practical Exam (50%).

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 (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. 

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 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.