Business Analytics

Abstract

In this course, students learn to plan, implement, and evaluate modern business analytics to generate value from data for society, organizations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage – and launching innovative services by leveraging the growing amounts of data and methods, such as machine learning.

Objective

Overall learning goal By the end of the course, students will be able to plan, implement and evaluate modern business analytics in applied settings in order to generate value from data for society, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage – and launching innovative serivces and products by leveraging the growing amounts of structured and unstructured data and methods, such as machine learning. Detailed breakdown by objective To achieve this overall goal, students should after participation being able to: Objective 1 (Managerial aspects): Understand the processes and challenges of modern business analytics-related projects • Identify and develop business ideas in corporations and organizations (e.g., startups) that create value using analytics • List implications for management when undertaking a project involving modern analytics • Apply the data mining process CRISP-DM to their project setting • Apply machine learning methods to generate predictions that support managers in their decision-making for the proposed business idea Objective 2 (Methodological challenges): Understand common methods for performing modern business analytics • Translate use cases of business analytics into a mathematical model formulation • Name common methods for business analytics, as well as their underlying concepts • Compare the properties of these models and perform performance assessment Objective 3 (Practical implementation): Performing actual evaluations of modern business analytics based on real-word datasets • Preprocess data in order to transform it into relational structures suited to machine learning modeling • Apply statistical software (e.g. “R” or Python) to explore data and perform modern business analytics using machine learning techniques (e.g., regression models, tree-based models, deep learning) • Evaluate the results in order to choose the best-performing machine learning method

Content

With the emergence of ubiquitous computing technology and machine learning methods in industrial applications, company decisions nowadays rely strongly on computer-aided “Business Analytics”. Business analytics refers to technologies that target how business information (or sometimes information in general) is collected, analyzed and presented. Combining these features results in software serving the purpose of providing better decision support for individuals, businesses and organizations. This course will teach what distinguishes the varying capabilities across business analytics – namely the underlying methods (e.g., machine learning). Participants will learn different strategies for data collection, data analysis, and data visualization. Sample approaches focus on machine learning modeling and machine learning pipelines to support business analytics projects. In particular, the course will teach the following themes: • Forecasting/Predicting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured and structured data to support the predictive performance? What are metrics to evaluate the performance of predictions? How to embed machine learning model predictions in business projects? • Data analysis: How can one derive explanatory power in order to study the response to an input? Note: the course provides the theoretical elements of modern business analytics projects and a brief introduction to selected machine learning methods. This provides then the basis for a project work where groups of students propose and implement analytics to business-relevant datasets. This project underlies eventually the grading.

Resources