HEC Montréal - MATH60033A
This course approaches quantitative methods in business research from a Data Science perspective. We explain the intuition behind a method, we discuss how researchers apply that method to understand real life problems, and we get our hands dirty by replicating using the following Data Science pipeline: Markdown, R, Git, Zotero, etc.
The goal is to expose students to the latest way of thinking when it comes to using data. This course will equip students with the necessary foundations to be, for instance, a business analyst. It will not be sufficient in itself, but it will present the building blocks that are necessary to achieve this goal.
We will approach the domain questions with an evidence-based perspective. For instance, we will use data and models to answer some of the following questions: How to construct a variable that measures country corruption? What does global really measure? How to design and interpret a questionnaire that captures CEO’s psychological barriers to internationalize?
Quantitative methods help researchers and business people approach real business problems and understand them with rigour. The objective of this course is twofold. First, after this course students will be familiar with basic statistics language as well as some applications of advanced statistics in the field of business research, so that once they face their thesis/supervised projects they can identify which method is most appropriate to answer their research question. Second, by the end of the course students will have some basic notions of how to work within a Data Science pipeline. For this purpose, five labs are designed to help students perform simple data analyses using Markdown, R, etc.
The complementarities of this course with other M. Sc. courses - e.g., Internationalization of the firm, Stratégie concurrentielle de multinationals - are very strong, as it will allow students to better understand papers that use quantitative methods in other courses. Also, we believe this course will allow students to gain a better competitive position in the job market, as there is an increasing number of firms that demand quantitative methods skills.
First, make sure you read the important information and details [here]
During this period, students will work on our communication platform as well as our analytical platform (optional).
| Session 1 | Session 2 | Session 3 | Session 4 | Session 5 | Session 6 |
|---|---|---|---|---|---|
| Monday, January 10 | Monday, January 17 | Monday, January 24 | Monday, January 31 | Monday, February 7 | Monday, February 14 |
| 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM |
| Session 7 | Session 8 | Session 9 | Session 10 | Session 11 | Session 12 |
|---|---|---|---|---|---|
| Monday, February 21 | Monday, March 14 | Monday, March 21 | Monday, March 28 | Monday, April 4 | Monday, April 11 |
| 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM |
| Session 1 | Session 2 | Session 3 | Session 4 | Session 5 | Session 6 |
|---|---|---|---|---|---|
| Friday, January 7 | Friday, January 14 | Friday, January 21 | Friday, January 28 | Friday, February 4 | Friday, February 11 |
| 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM |
| Session 7 | Session 8 | Session 9 | Session 10 | Session 11 | Session 12 |
|---|---|---|---|---|---|
| Friday, February 18 | Friday, March 11 | Friday, March 18 | Friday, March 25 | Friday, April 1 | Monday, April 11 |
| 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM | 8:30 AM - 11:30 AM |
| Lab 1 | Lab 2 | Lab 3 | Lab 4 | Lab 5 | Lab 6 |
|---|---|---|---|---|---|
| Wednesday, January 12 | Wednesday, January 19 | Wednesday, January 26 | Wednesday, February 2 | Wednesday, February 9 | Wednesday, February 16 |
| 4:30 PM - 7:30 PM | 4:30 PM - 7:30 PM | 4:30 PM - 7:30 PM | 4:30 PM - 7:30 PM | 4:30 PM - 7:30 PM | 4:30 PM - 7:30 PM |
Mid-term exam: Monday, March 7 from 8:30 AM to 11:30 AM
Final exam: Thursday, April 14 from 9:00 AM to 12:00 PM
[Deploy some information by clicking on the triangle-shaped pictogram]
The first half of the class will give students an overview of the course. All parties will introduce themselves, and we will present the material and topics we will cover over the term and the evaluation criteria. The second half of the class will introduce key statistical concepts.
Fundamentals in statistics.
Correlation, multiple regression, model assumptions, collinearity.
Model selection based on AIC, BIC and adjusted R2, stepwise methods, generalized linear models such as logistic regression.
Specifities about cross-section time-series analyis.
Multinomial regression, cumulative logit regression, other generalized linear models.
Selection of the number of factors, factor rotation, creation of new variables, Cronbach’s alpha.
Data biases, model biases, human biases.
Lab #1
Lab #2
Lab #3
Lab #4
Lab #5
Lab #6
In this course, you will have access to some of the tools we use on our data science platform in our lab http://lab.warin.ca. The use of these tools requires adherence to our Honour Code. This comes on top of the school’s regulations [here]
It is essential to foster a supportive e-learning environment.
In our lab, we believe it is essential that all participants embody and uphold our community values in order to foster a supportive online learning environment where individuals can have open discussion, reflect on their thinking and learn from each other.
A code of honour, ethics and respect.
Our Honour Code complements the Community Values Statement and reflects the commitment of participants as members of the learning community to participate in, foster and sustain the learning model of our laboratory.
Before the session:
That’s all !