Introduction to Business and Social Data Science

About

These are exciting times to work with data. Data science—bridging statistics, computer science, and substantive area expertise, has become an integral part of decision-making since the availability and diversity of data sources have recently increased at an unprecedented pace. This course will provide you with a very applied introductory knowledge about working with data to understand and inform various business and social decisions. Upon completing the course, you should be able to understand the techniques introduced in the course and apply them to new data and specific problems. The course content covers two broader areas:

1. Working with data: data wrangling, transformations, summary, text-as-data, & visualization.

2. Machine learning techniques and principles: such as bias-variance trade-off, out-of-sample prediction, regression, classification, regularization, and so on.

For our teaching, we will introduce a particular question or application and offer data driven solutions based on various techniques. Throughout the course we will follow an applied, hands-on approach, always working on implementation and coding (in R). Hence, you will spend a substantial amount of time working with software. All course content and activities will be based on data (or type of data) often used in private and public organizations, from various country, firm and individual level sources.

Course leader

Zoltan Fazekas - Department of International Economics, Goverment and Business

Target group

This is a bachelor level course. CBS Summer University courses at Copenhagen Business School is open to all and welcomes domestic and international students as well as professionals.

Course aim

Be able to load, process, and transform data stemming from different sources and structured in different ways.

Summarize data and apply visualization techniques to explore and present data.

Identify, apply and compare suitable introductory machine learning techniques to business and social data.

Effectively communicate your results, processes, and share well documented and formatted code, following best practices.

Formulate data driven conclusions for social and business applications, while incorporating considerations related to uncertainty and causality.

Fee info