20593 - INNOVATION AND MARKETING ANALYTICS

To feel comfortable in this course you should know basic Python.

This course is offered in the second semester of the MSc in Data Science and Business Analytics (DS & BA). By then, students have deep knowledge of different programming languages such as Python and R, as well as of statistical models to identify correlational and causal relations in data. The course is divided into two main parts. In the first part, students will learn how to gather data that can be used to conduct innovation and marketing activities. In the second part, students will learn how to analyze this data using frontier of research statistical techniques.

Part 1: Computational techniques in marketing

Part 2: Data analysis for marketing applications

• Understand the concept of digital trace data
• Obtain digital trace data
• Perform data wrangling
• Perform data visualization
• Analyze data to test Hypothesis

• Obtain digital trace data
• Conduct data visualization and data wrangling tasks
• Perform basic text analysis
• Perform traditional marketing research analyses using Big Data

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Individual assignments
• Group assignments

During the course, in addition to face-to-face lectures, the following activities are completed:

• Exercises on real data collected by students or provided by the instructors. These exercise allow students to practice the concepts learned in class.
• Individual and group assignments that allow students to use all the knowledge acquired throughout the course.

• We will ask students to deliver take-home problem set(s) that will put in practice some of the concepts learned in the class.
• The group project consists of adopting the methodologies learned in class to real company problems. The projects are used to verify the ability of students to apply the knowledge developed during the course and how to present it effectively, including how to propose hypotheses, collecting corresponding data, wrangling, and analyzing the data.
• The exam is held in written form. It is made up of questions referring to the concepts, models and cases discussed in class. The questions aim to verify learning of the analytical and management abilities and their correct comprehension, and to assess the ability to apply the knowledge that students learned during the course.

The assessment method for non-attending students is based on a final exam in written form. It is made up of questions referring to the concepts, models and cases contained in the textbooks and exam materials. The questions aim to verify learning of the analytical and management abilities and their correct comprehension, and to assess the ability to apply the knowledge that students learned when studying the course material.

Class notes and articles from academic journals distributed by the instructors and posted on Bboard.

• VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data.  O'Reilly Media, Inc.
• Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, Inc.