Quantitative Methods

MBA program course

Description

Course description:

In our data-driven world, it is hard to imagine a modern manager who does not know the basic tools of quantitative analysis. The course will include a necessary minimum of theory (30% of the time) and a lot of practical examples using real data from business, management, and finance. We will begin with a motivational example of forecasting sales using Excel Add-in. Then we will learn how to summarize data and build informative graphs. We will next briefly discuss the probability theory and mechanics behind Ordinary Linear Squares (OLS) regression. Linear Regression is a powerful quantitative methods which will be covered in depth using multiple datasets from Harvard Business Publishing. Then we will consider two extensions to regression: linear probability model to analyze binary data and time series models. The course will end with a brief introduction of advanced topics of big data analysis in R/RStudio that will lay down a foundation for future independent learning by the most motivated students.

 

Students are expected to bring a laptop with an installed Excel 2013 (or later) English version. While group work on assignments is encouraged, 50% of the Final exam will be based on empirical exercises so each student should be proficient in Excel by the end of the course.

 

Learning outcomes:

At the end of this course, a student should be able to:

 

  • Analyze and describe business data in Excel
  • Be familiar with techniques of OLS estimation
  • Understand basic probability theory
  • Be able to run linear regression using Excel Add-in
  • Apply regression to forecast business outcomes
  • Understand the nature of qualitative and time-series data
  • Have a basic understanding of advanced data analysis in R using machine learning techniques

 

Course language: English

 Price: UAH 30 000

 

Course outline:

 

  • Introduction; Motivational example: gas sales; Getting started with Excel
  • Descriptive statistics and graphs
  • Summarizing data; Pivot tables
  • Introduction to probability theory
  • Ordinary Least Squares regression
  • Hypothesis testing in OLS; Practical examples
  • Estimating and forecasting in linear regression; Business problems
  • Predicting qualitative data: linear probability model, logit and probit
  • Linear regression in time series context
  • Estimating time series models; Examples from financial time series
  • Advanced topics: Introduction to big data and R/RStudio
  • Advanced topics: Machine learning in R/RStudio for improved forecasting
  • Final exam

Contact us to get more details:

Viber, WhatsApp +380 67 441 01 11, [email protected]