Econometrics and Statistics for Business in R & Python
Econometrics and Statistics for Business in R & Python, available at $84.99, has an average rating of 4.4, with 151 lectures, based on 593 reviews, and has 5564 subscribers.
You will learn about Understand the application of econometric techniques in business settings Apply Google's Causal Impact to measure the effect of an intervention on a time series. Code econometric techniques in R and Python from scratch. Solve real business or economic problems using econometric techniques. Use propensity score matching to compare outcomes between groups while controlling for confounding variables. Develop an intuitive understanding of Difference-in-differences, Google's Causal Impact, Granger Causality, Propensity Score Matching, and CHAID Perform Granger causality to test for causality between two time series. Develop intuition for econometric techniques through business case studies. Practice coding and applying econometric techniques through challenging and interesting problems. Understand and apply basic statistical concepts and techniques in real-life business cases This course is ideal for individuals who are Students or recent graduates interested in Econometrics and Data Science or Data Scientists that would like to learn econometrics or Business Analysts wanting to make a difference in their current job or People curious about Econometrics and Data Science or Professionals who would like to know more about analytics It is particularly useful for Students or recent graduates interested in Econometrics and Data Science or Data Scientists that would like to learn econometrics or Business Analysts wanting to make a difference in their current job or People curious about Econometrics and Data Science or Professionals who would like to know more about analytics.
Enroll now: Econometrics and Statistics for Business in R & Python
Summary
Title: Econometrics and Statistics for Business in R & Python
Price: $84.99
Average Rating: 4.4
Number of Lectures: 151
Number of Published Lectures: 151
Number of Curriculum Items: 151
Number of Published Curriculum Objects: 151
Original Price: €219.99
Quality Status: approved
Status: Live
What You Will Learn
- Understand the application of econometric techniques in business settings
- Apply Google's Causal Impact to measure the effect of an intervention on a time series.
- Code econometric techniques in R and Python from scratch.
- Solve real business or economic problems using econometric techniques.
- Use propensity score matching to compare outcomes between groups while controlling for confounding variables.
- Develop an intuitive understanding of Difference-in-differences, Google's Causal Impact, Granger Causality, Propensity Score Matching, and CHAID
- Perform Granger causality to test for causality between two time series.
- Develop intuition for econometric techniques through business case studies.
- Practice coding and applying econometric techniques through challenging and interesting problems.
- Understand and apply basic statistical concepts and techniques in real-life business cases
Who Should Attend
- Students or recent graduates interested in Econometrics and Data Science
- Data Scientists that would like to learn econometrics
- Business Analysts wanting to make a difference in their current job
- People curious about Econometrics and Data Science
- Professionals who would like to know more about analytics
Target Audiences
- Students or recent graduates interested in Econometrics and Data Science
- Data Scientists that would like to learn econometrics
- Business Analysts wanting to make a difference in their current job
- People curious about Econometrics and Data Science
- Professionals who would like to know more about analytics
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 AND CAUSAL INFERENCE 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 departmentscan use these causal techniques. Here is the list:
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Difference-in-differences
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Google’s Causal Impact
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Granger Causality
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Propensity Score Matching
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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 experienceand 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:
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Impact of M&A on companies.
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Understanding how weather influences sales.
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Measuring the impact of brand campaigns.
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Whether Influencer or Social Media Marketing results in sales.
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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:
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Measuring the impact of the Cambridge Analytica Scandal on Facebook’s stock price.
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Assessing the results of giving training to employees.
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Challenge the idea that increasing the minimum wage decreases employment.
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Ranking the drivers on why people quit their jobs.
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Solving the thousand-year-old riddle of who came first: “Chicken or the egg?”.
4| HANDS-ON CODING
We will code together, in R and Python. 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
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course introduction and structure
Lecture 2: Course content
Lecture 3: Installing R and RStudio
Lecture 4: Installing Python and Spyder
Lecture 5: How to get more from the course
Lecture 6: Future of this course and reviews
Chapter 2: Difference-in-differences – Intuition tutorial – Case Study 1
Lecture 1: Difference-in-differences use cases
Lecture 2: Difference-in-Differences framework
Lecture 3: Modelling Difference-in-differences
Lecture 4: Difference-in-differences assumptions
Lecture 5: Difference-in-differences step by step guide
Lecture 6: Linear Regression crash course
Lecture 7: Linear Regression output summary
Lecture 8: Dummy variable trap
Chapter 3: Difference-in-differences – R tutorial – Case Study 1
Lecture 1: Getting dataset and code templates folder
Lecture 2: Intro to RStudio and data loading
Lecture 3: Dealing with NAs part 1
Lecture 4: Dealing with NAs part 2
Lecture 5: First linear regression model
Lecture 6: Second linear regression model and dummy variable trap
Lecture 7: Last linear regression
Lecture 8: Presenting results
Chapter 4: Difference-in-differences – Python tutorial – Case Study 1
Lecture 1: Getting datasets and code templates folder
Lecture 2: Intro to Spyder and loading data
Lecture 3: Dealing with NAs
Lecture 4: Isolating X and Y variables
Lecture 5: First linear regression model
Lecture 6: Second linear regression model and dummy variable trap
Lecture 7: Last linear regression
Lecture 8: Your feedback is valuable
Chapter 5: Difference-in-differences – Intuition tutorial – Case Study 2
Lecture 1: Introducing second case study
Lecture 2: Logistic Regression crash course
Lecture 3: Placebo test mechanics
Chapter 6: Difference-in-differences – R tutorial – Case Study 2
Lecture 1: Getting datasets and code templates folder
Lecture 2: Loading data and inspecting it
Lecture 3: Defining variables
Lecture 4: First Logistic Regression in R
Lecture 5: Second Logistic Regression Model
Lecture 6: Visualizing results
Lecture 7: Preparing variables and dataset for placebo experiment
Lecture 8: Logistic Regression and Placebo experiment
Chapter 7: Difference-in-differences – Python tutorial – Case Study 2
Lecture 1: Getting datasets and code templates folder
Lecture 2: Loading data and inspecting it
Lecture 3: Creating dummy variables
Lecture 4: Splitting X and Y variables
Lecture 5: First Logistic Regression in Python
Lecture 6: Second Logistic Regression
Lecture 7: Preparing dataset for placebo experiment
Lecture 8: Logistic Regression and Placebo experiment
Chapter 8: Google Causal Impact – Intuition tutorial
Lecture 1: Introducing Causal Impact
Lecture 2: Value added of Causal Impact
Lecture 3: Step by step application guide
Lecture 4: Case study briefing
Chapter 9: Google Causal Impact – R tutorial
Lecture 1: Getting dataset and code templates folder
Lecture 2: Code Update
Lecture 3: Loading Facebook's stock price
Lecture 4: Loading more stock prices
Lecture 5: Plotting stock prices
Lecture 6: Correlation Matrix
Lecture 7: Choosing control group
Lecture 8: Preparing dataset to run Causal Impact
Lecture 9: Calculating the impact
Lecture 10: Interpreting Causal Impact results
Chapter 10: Google Causal Impact – Python tutorial
Lecture 1: Getting datasets and code templates folder
Lecture 2: Code Change
Lecture 3: Loading Facebook's stock price
Lecture 4: Preparing stock price dataset
Lecture 5: Plotting stock prices
Lecture 6: Correlation Matrix
Lecture 7: Finishing up the control groups
Lecture 8: Preparing dataset to run Causal Impact
Lecture 9: Running Causal Impact
Lecture 10: Interpreting Causal Impact results
Chapter 11: Granger Causality – Intuition tutorial
Lecture 1: Granger Causality use cases
Lecture 2: Problem statement
Lecture 3: Correlation is not causality!
Lecture 4: Granger Causality framework
Lecture 5: Stationarity
Lecture 6: Granger Causality step by step guide and case study briefing
Chapter 12: Granger Causality – R tutorial
Lecture 1: Getting dataset and code templates folder
Lecture 2: Loading and inspecting data
Lecture 3: Plotting time series
Lecture 4: Stationarity check
Lecture 5: Applying Granger Causality
Lecture 6: Optimal number of lags and for loop part 1
Lecture 7: Optimal number of lags and for loop part 2
Chapter 13: Granger Causality – Python tutorial
Lecture 1: Getting datasets and code templates folder
Instructors
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Diogo Alves de Resende
Analytics and Data Science expert
Rating Distribution
- 1 stars: 5 votes
- 2 stars: 11 votes
- 3 stars: 49 votes
- 4 stars: 182 votes
- 5 stars: 346 votes
Frequently Asked Questions
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