This course will provide students with an introduction into business analytics, with a focus on strategic decision making. The amount of available data outpaces our ability to consume it while the technologies used to collect and interpret that data evolve quickly. Businesses everywhere need experts to capture, mine and interpret the findings. Companies and individuals who can use this data together with analytics give themselves an edge over the competition.

In this course, students will learn how business analytics can help to improve business processes and strategic decision making. Building on theoretical foundations of a business analytics lifecycle, it will use real-world examples and immersive practical experience to showcase the potential of strategic business analytics in decision making. The course will discuss different applications of business analytics, cover selected topics in regression and classification models, and explore model evaluation. As part of this course, students will learn how to use the open-source statistical toolkit R to implement and evaluate business analytics problems.

On successful completion of the course, you should be able to:

- demonstrate an understanding of different concepts and methods in business analytics

- describe how business analytics can support strategic decision making and be able to identify analytics opportunities

- demonstrate essential technical insights into the fundamentals of advanced data analysis and the ability to select the best type of analytics methods for a specific problem

- understand how to interpret and present the results of advanced data analysis

- demonstrate the ability to use statistical computer software to implement and evaluate a business analytics problem

Lectures will use a combination of theoretical foundations and empirical examples to introduce and discuss different aspects, opportunities, and challenges of successfully using business analytics in strategic decision making. Lectures also include hands-on demonstrations of analytics implementations using a statistical computer package.

The lectures will focus on real-world problems and applications of strategic business analytics. Students will learn what data and algorithms are required to make strategic decisions in business environments. Besides theoretical input, students will be asked to complete problem sets as assignments with the ongoing support of the instructor. These will enable students to learn how to handle and analyze data, as well as critically interpret results. Discussion of these problem sets and other examples will take place in class, mainly during the second-half of each lecture. In a mini project, students will analyze publicly-available data sets using ready-to-adapt R code provided by the lecturer. At the end of the course, students will present their results and what they have learned from the process.

Mini-Project: Applied Strategic Business Analytics

In groups, students will analyze publicly-available data sets using the ready-to-adapt R templates provided in the hands-on course sessions. The feasibility of proper data analysis and visualization of outcomes will be ensured by the ongoing support of the instructors. In a final presentation, students will present their results and what they have learned from the process.