Data Analytics (MSB103)

"There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT). Over the last two years alone 90 percent of the data in the world was generated." (Marr, 2018). In today’s knowledge economy, data is frequently seen as the most crucial resource ("the new oil"). In order to make sound, justifiable, and informed decisions, managers must be able to quickly access, process, and analyze up-to-date information on a small- and large-scale basis. This course offers training for such knowledge and skills, through the use of statistical models, real data from around the world, and R software. Thus, students will learn how to uncover the hidden patterns and narratives behind data in a scientific manner that will form the basis for decision-makers in this data-driven world.

Subject areas that are most likely covered are:

• Introduction to R and basic programming
• Data visualizations
• Ordinary least squares and diagnostics
• Logistic regression and classification
• Panel regression
• Math and stat review
• and more

Knowledge

On completion of the course, students will have gained knowledge of:

• Basic programming in R
• Using R to analyze data and generate attractive presentations
• Constructing and estimating appropriate statistical models

Skills

Upon completion of this course, students will be able to:

• Use R to construct a variety of measures, variables, and visualizations and analyze empirical data
• Assess and employ basic multivariate statistical models
• Evaluate and interpret statistical results of basic multivariate statistical models

In this course, you will learn through a combination of traditional lectures, exercises and individual study. Lectures provide the basic theoretical knowledge behind the methods. Students will acquire practical knowledge of (1) basic programming and working with R; (2) handling different datasets; (3) setting up problems and running appropriate statistical models; (4) properly interpreting empirical results. Students are required to obtain the necessary knowledge through self-study of different materials including videos, textbook chapters and lecture slides.

Expectations: 280 ECTS hours divided between lectures, in-class and out-of-class (group) work, seminars and independent study.