Course 2022-2023 a.y.

20593 - INNOVATION AND MARKETING ANALYTICS

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


Class-group lessons delivered  on campus

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: 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


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...
  • 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

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.
  • 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.

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 02/02/2023 16:56