Econometrics has horrible fame. The complex theorems, combined with boring classes where it feels like you are learning Greek, give every student nightmares. This course stays away from that. It will focus on (1) giving you the intuition and tools to apply the techniques learned, (2) making sure everything that you learn is actionable in your career, and (3) offer you a tool kit of peer-reviewed econometric causal inference techniques that will make you stand out and give you the ability to answer the tough questions.
WHY ECONOMETRICS FOR BUSINESS IN R AND Python?
In each section, you will learn a new technique. The learning process is split into three parts. The first is an overview of Use Cases. Drawing from business literature and my own experience, I will show examples where each Econometric technique has been applied. The goal here is to show that Econometric methods are actionable. The second part is the Intuition tutorials. The aim is for you to understand why the technique makes sense. All intuition tutorials are based on business situations. The last part is the Practice tutorials, where we will code and solve a business or economic problem. There will be at least one practice tutorial per section.
Below are 4 points on why this course is not only relevant but also stands out from others.
1| THOROUGH COURSE STRUCTURE OF MOST IMPACTFUL ECONOMETRIC TECHNIQUES
The techniques in this course are the ones I believe will be most impactful in your career. Like HR, Marketing, Finance, or Operations, all company departments can use these causal techniques. Here is the list:
Difference-in-differences
Google's Causal Impact
Granger Causality
Propensity Score Matching
CHAID
2| BUSINESS EXAMPLES TO FOSTER INTUITION
Each section starts with an overview of business cases and studies where each econometric technique has been used. I will use examples that come from my own professional experience and business literature. The aim is to give you the intuition where to apply them in your current job. By the end of each intuition tutorial, you will be able to easily explain the concepts to your colleagues, manager, and stakeholders.
One of the benefits of giving actual business problems as examples is that you will find similar or even equal issues in your current company. In turn, this enables you to apply what you have learned immediately. Here are some examples:
Impact of M&A on companies.
Understanding how weather influences sales.
Measuring the impact of brand campaigns.
Whether Influencer or Social Media Marketing results in sales.
Investigating the drivers of customer satisfaction.
3| CHALLENGING AND INTERESTING PROBLEMS TO APPLY WHAT YOU LEARNED
For each section, we will have at least one real business or economic dataset. We will apply what we learned during the intuition tutorials.
Here are some examples of problems we will solve and code together:
Measuring the impact of the Cambridge Analytica Scandal on Facebook's stock price.
Assessing the results of giving training to employees.
Challenge the idea that increasing the minimum wage decreases employment.
Ranking the drivers on why people quit their jobs.
Solving the thousand-year-old riddle of who came first: "Chicken or the egg?".
4| HANDS-ON CODING
We will code together. In every single practice tutorial, we will start from scratch, building the code line by line. As also an online coding student, I feel this has been the easiest way to learn.
On top, the code will be built so that you download it and apply the causal inference techniques in your work and projects. Additionally, I will explain what you have to change to use in your dataset and solve the problem you have at hand.
Econometrics for Business in R and Python is a course that naturally extends into your career.
***SUMMARY
The course is packed with use cases, intuition tutorials, hands-on coding, and, most importantly, is actionable in your career.
Feel free to reach out if you have any questions, and I hope to see you inside!
Diogo
