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Econometrics for Business in R and Python

👉   Econometrics for Business in R and Python | Udemy Ned

But still, Python is not useful for econometrics and communication, and also for business analytics. On the other hand, R is developed by academics and scientists. It is specially designed for machine learning and data science.


What you'll learn
  • Econometric use cases in the business world
  • Difference-in-differences
  • Google's Causal Impact
  • Granger Causality
  • Propensity Score Matching
  • R for Econometrics
  • Python for Econometrics
  • Regressions and t-tests

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 way 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.


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 together 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.


The techniques in this course are the ones I believe will be most impactful in your career. All company departments, like HR, Marketing, Finance, or Operations, can use these causal techniques. Here is the list:

  1. Difference-in-differences

  2. Google's Causal Impact

  3. Granger Causality

  4. Propensity Score Matching

  5. CHAID


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 from 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:

  1. Impact of M&A on companies.

  2. Understanding how weather influences sales.

  3. Measuring the impact of brand campaigns.

  4. Whether Influencer or Social Media Marketing results in sales.

  5. Investigating the drivers of customer satisfaction.


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:

  1. Measuring the impact of the Cambridge Analytica Scandal on Facebook's stock price.

  2. Assessing the results of giving training to employees.

  3. Challenge the idea that increasing the minimum wage decreases employment.

  4. Ranking the drivers on why people quit their jobs.

  5. Solving the thousand-year-old riddle of who came first: "Chicken or the egg?".


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 in such a way 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.


The course packed with use cases, intuition tutorials, hands-on coding, and, most importantly, is actionable in your career.

Feel free to reach out in case you have any questions, and I hope to see you inside!


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