Introduction

Course structure

This course combines asynchronous teaching elements (e.g., lecture notes on this website) with synchronous elements (e.g., weekly in-person interactive sessions on campus). The syllabus consists of three main parts, as reflected by the structure of this website:

  1. Lecture notes: the lecture part will explain the theory behind the concepts and methods and provide you with example applications using the statistical software R.
  2. Individual assignments: the individual assignments require you to apply the acquired knowledge to new data sets.
  3. Group project: in the group you will design and conduct your own market research project and transfer the knowledge to a real business setting.

Contents

In-person lecture

The contents on this website are divided into weekly readings. To be able to follow the curriculum and complete the assignments, you need to read the materials assigned for the respective week. The relevant chapters are indicated in the table below. The weekly readings will be complemented with weekly interactive sessions in the PC lab, which provide you with an opportunity to ask questions about the assigned readings. Please note that you need to go through the materials on your own in the week before the respective session. For example, chapters 2, 3 & 4 will be discussed in the second session. The dates and times for the classroom sessions are indicated in the table below for each group separately. It is highly recommended to prepare questions or comments about the materials for these sessions that you think might be interesting and helpful to the class. As a preparation for the in-class discussions and quizzes, you should go through the Learning check section at the end of each chapter. By working through these questions, you may self-assess your progress and identify knowledge gaps regarding the materials that were assigned for the previous week. Learning checks will also be performed in class as means of tracking participation as well as students’ understanding of the material.


Lecture dates
Date Day Time Room Topics Chapters
Oct 17 Thursday 08:00 AM - 11:00 AM TC.5.27 Introduction to the course
Basic concepts of research design 		1, except:
1.2.3.1.,
1.2.3.1.2.1,
1.2.3.2.2
Oct 24 Thursday 08:00 AM - 11:00 AM TC.5.27 Introduction to R & R Markdown I 2, 3, 4
Nov 7 Thursday 08:00 AM - 11:00 AM TC.5.27 Introduction to R & R Markdown II 2, 3, 4
Nov 14 Thursday 08:00 AM - 11:00 AM TC.5.27 Introduction to inferential statistics 5
Nov 21 Thursday 08:00 AM - 11:00 AM TC.5.27 Supervised learning I 6
Nov 28 Thursday 08:00 AM - 11:00 AM TC.5.27 Supervised learning II 6
Dec 5 Thursday 08:00 AM - 11:00 AM TC.5.27 Experimental design
Group project coaching 		1: 1.2.3.1., 1.2.3.1.2.1, 1.2.3.2.2
Dec 12 Thursday 08:00 AM - 10:00 AM D5.0.002 Exam –


The self-study format might pose challenges to the learning process because we cannot troubleshoot in person outside of the classroom sessions. Remember that it is very unlikely that you are the only student encountering a particular problem. So please make use of the Discussions on Canvas (see below) to interact with your peers or ask me questions so that everyone else will benefit from the answer (there are no stupid questions!). In case you cannot get answers to address a specific problem, I will be available during the in-person classroom sessions for coaching.

Individual assignments

There will be 2 individual assignments. These assignments need to be submitted in the R Markdown format (see chapter 8) via Canvas. There will be an in-class session dedicated to the R Markdown reporting format, when the first homework is assigned.


Assignment schedule
Assignment Assigned Submission
Assignment 1: R Basics TBA / see Canvas TBA / see Canvas
Assignment 2: Supervised learning TBA / see Canvas TBA / see Canvas


Group project

The group project consists of an extended analysis of a data set using the methods we covered in the course and the reporting of the results using the R markdown format. The submission date for the group project will be announced soon.

Again, please make sure that you have exhausted all other resources to solve a particular problem, such as the online tutorial, the forum on Canvas, and other web resources (see below) before you schedule a coaching session. If you feel that other students might have similar questions and would benefit from an answer to a particular question, you should post the question in the forum on Canvas.

Grading

Grading is based on the following components:

  • Market research group project (data analysis & reporting): 40%
  • Individual take-home computer exercises (statistical analysis of data sets; 2 assignments accounting for 10% each: 20%
  • Final online exam (concepts & methods): 30%
  • Class participation (weekly quizzes; quantity & quality of contributions during the weekly sessions): 10%

The final exam will take place on December 12, 2024 from 08:00 AM - 10:00 AM in room D5.0.002. Details about the setup of the exam will be provided in the course. The exam covers the concepts and methods (no coding) that we learn in this course; some sample questions from the previous years will be provided to you.

In order to ensure equal contribution to the group project, its grade consists of individual component (1/3) and group component (2/3).

To successfully pass this course, your weighted final grade needs to exceed 60%.

Course materials

Main reference

The main reference for this course is this website along with the corresponding slides and the pre-recorded video lectures. The relevant materials for each week are indicated in the tables above. The aim of the materials is to condense the contents and direct your attention to the most relevant aspects. This should enable students to study the materials on their own and we can focus our attention during the classroom sessions on clarifying points that require further discussion.

At the end of each chapter, you will find a section with references. It is highly recommended that you consult these references for further clarification in case you require additional information on a topic.

Further readings

DSUR cover  ISL cover  R4mra cover  advr cover  tmwr cover  Rpacks cover Rpacks cover Rgraph cover

In addition to these lecture notes, there are many excellent books available (many of them for free) that focus on different aspects of R. In fact, there are so many free resources available by now that a team of R programmers has set up a website that provides an overview over the available resources by topic. You can find this overview here: Big Book of R.

In case you would like to learn more about the capabilities of R related to the contents of this course, I can particularly recommend the following books:

  • “R for Data Science” An excellent book by Hadley Wickham, which introduces you to R as a tool for doing data science, focusing on a consistent set of packages known as the tidyverse. [FREE online version]

  • “An Introduction to Statistical Learning” This book provides an introduction to statistical learning methods and covers basic methods (e.g., linear regression) as well as more advanced methods (e.g., Support Vector Machines). [FREE online version]

  • “R for Marketing Research and Analytics” A great book that is designed to teach R to marketing practitioners and data scientists.

  • “Statistical Inference via Data Science” Another great book covering topics around Statistical Inference. [FREE online version]

  • “Text Mining with R” This book explains how you can analyze unstructured data (texts) using R. [FREE online version]

  • “Advanced R” Another great book written by Hadley Wickham. Explains more advanced R concepts. [FREE online version]

  • “Hands-On Machine Learning with R” A great reference to learn about machine learning methods in R. The book favors a hands-on approach, growing an intuitive understanding of machine learning through concrete examples and little bit of theory.[FREE online version]

  • “Hands-On Data Science for Marketing” Another good reference regarding Data Science for Marketing. [FREE Code exercises]

  • “R Markdown” A great book about the reporting format ‘R Markdown’, which we will also use for the assignments in this course. [FREE Code exercises]

  • “R Packages” A book which teaches you how to make the most of R’s fantastic package system. [FREE online version]

  • “R Graphics Cookbook” A practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly. [FREE online version]

  • “Using R For Introductory Econometrics” This book covers a nice introduction to R with a focus on the implementation of standard tools and methods used in econometrics. [FREE online version]

  • “Data Science in a Box” Another book covering topics around Data Science using R. [FREE online version]

  • “Efficient R Programming” A good reference to learn efficient workflows using R. [FREE online version]

  • “Discovering Statistics Using R” (Field, A., Miles, J., & Field Zoe, 2012, 1st Edtn.) This textbook offers an accessible and comprehensive introduction to statistics.

Discussion forum

I strongly encourage you to ask your questions via the online forum (Discussions section) on Canvas. The purpose of the forum is to allow you to discuss questions related to the contents with your classmates and me. Please make use of this forum as much as possible and ask questions if something remained unclear. Remember that there are no stupid questions! And if you know the answer to a question that is asked in the forum, it is also a good exercise to explain the concepts to your classmates.

Other web-resources

Contact

Feel free to send me an email in case you have questions. However, please make sure that you have exhausted all other resources to solve a particular problem, such as the online tutorial, the forum on Canvas, and other web resources (see below) before you schedule a coaching session. If you feel that other students might have similar questions and would benefit from an answer to a particular question, you should post the question in the forum on Canvas.

Acknowledgements

This tutorial is supported through Digital Learning Project Funding by WU Vienna. None of the materials covered in this tutorial are new. We intend to provide a summary of existing methods from a marketing research perspective and cite the corresponding sources. If you should have any comments or suggestions, please contact us through the github page of this course.