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Course 2023-2024 a.y.

20593 - INNOVATION AND MARKETING ANALYTICS

DSBA
Department of Marketing

Course taught in English



Go to class group/s: 23

DSBA (6 credits - II sem. - OB  |  SECS-P/08)
Course Director:
QIAONI SHI

Classes: 23 (II sem.)
Instructors:
Class 23: QIAONI SHI


Synchronous Blended: Lessons in synchronous mode in the classroom (for a maximum of one hour per credit in remote mode)

Suggested background knowledge

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


Mission & Content Summary
MISSION

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.

CONTENT SUMMARY

Part 1: Computational techniques in marketing

Part 2: Data analysis for marketing applications


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Understand the concept of digital trace data
  • Obtain digital trace data
  • Perform data wrangling
  • Perform data visualization
  • Analyze data to test Hypothesis
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Obtain digital trace data
  • Conduct data visualization and data wrangling tasks
  • Perform basic text analysis
  • Perform traditional marketing research analyses using Big Data

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

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.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING STUDENTS
    • 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.
    NOT ATTENDING STUDENTS

    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.


    Teaching materials
    ATTENDING STUDENTS

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

    NOT ATTENDING STUDENTS
    • 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.
    Last change 21/12/2023 10:52