Introduction

Course structure

This course combines asynchronous teaching elements (e.g., texts and pre-recorded videos on this website) with synchronous elements (e.g., weekly in-person interactive sessions in the PC Lab). 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.

The general approach is that students will first familiarize themselves with the contents by going through the materials on their own. This self-study process is complemented with in-person weekly interactive sessions in the PC lab, which provide ample opportunities to ask questions and clarify points that require further discussion. The schedule for each of the three parts will be explained below.

Schedule

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, 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.


Lecture dates (5138)
Date Day Time Room Topics Chapters
Oct 12 Thursday 04:00PM - 08:00PM TC.-1.61 Introduction to the course
Basic concepts 		1
Oct 19 Thursday 04:00PM - 08:00PM TC.-1.61 Introduction to R & R Markdown 2, 3, 4
Nov 09 Thursday 04:00PM - 08:00PM TC.-1.61 Introduction to inferential statistics
Hypothesis testing 		5
Nov 16 Thursday 03:00PM - 08:00PM TC.-1.61 Supervised learning 6
Nov 23 Thursday 03:00PM - 08:00PM TC.-1.61 Unsupervised learning 7
Nov 30 Thursday 03:00PM - 05:00PM Online Exam –


We understand that 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 forum on via Canvas (see below) to interact with your peers or ask us 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, we will be available during the in-person classroom sessions for coaching.

Individual assignments

There will be 3 individual assignments. These assignments need to be submitted in the R Markdown format (see chapter 8) via Canvas. There will be a coaching session dedicated to the R Markdown reporting format in the second session, when the first homework is assigned.


Assignment schedule - Group A (2206)
Assignment Assigned Submission
Assignment 1: R Basics Oct 19 Nov 2, 11:59PM
Assignment 2: Supervised learning Nov 16 Nov 22, 11:59PM
Assignment 3: Unsupervised learning Nov 23 Dec 7, 11:59PM


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 is on December 15, 2023.

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) [weight: 30%]
  • Individual take-home computer exercises (statistical analysis of data sets; 3 assignments accounting for 10% each [weight: 30%]
  • Final online exam (concepts & methods) [weight: 30%]
  • Class participation (quantity & quality of contributions during the weekly sessions, contributions in the online forum, etc.) [weight: 10%]

These grading components will be weighted with the respective weights to arrive at the final grade percentage.

The final exam will take place online on November 30, 2023 from 03:00PM - 05:00PM. Details about the setup of the exam will be provided in the course. The exam covers questions about the concepts and methods (no coding) and I will provide example exams from the previous years to give you an idea about what type of questions you can expect.

To ensure an equal contribution of group members for the group assignment, a peer assessment will be conducted among group members, which enters into the computation of the individual grades for the project. This means that the members of a group are required to assess other students regarding their relative contribution.

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

We strongly encourage you to ask your questions via the online forum on the course page on the WU learning platform. The purpose of the forum is to allow you to discuss questions related to the contents with your class mates and us. 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.

## Warning: package 'knitr' was built under R version 4.2.3