Course 2022-2023 a.y.

Part 1: Data acquisition

 Acquisition of digital trace data

Part 2: Unstructured Data

Regular expression and text analysis

Part 3: Structured Data Analysis

Data exploration and data visualization

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

• An understanding of what is digital trace data and how to obtain it

• Familiarity with data visualization and data wrangling

• An understanding of basic text analysis

• Performing traditional marketing research analyses through 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.

• 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.