Executive Business Analytics

Abstract

This course will combine cutting edge thinking about Artificial Intelligence & Machine Learning, with application use cases and a practical framework that will enable participants to determine and plan their own workplace application.The focus is less on the how (i.e. how the algorithms function) but more on techniques to identify suitable use cases.

Objective

Objective 1 (Managerial aspects): Understand the processes and challenges of analytics-related projects • Being able to identify applications for analytics in corporations and organizations that create value • Being able to list implications for management when undertaking a project involving business analytics • Being able to to describe the data mining process CRISP-DM to their actual setting Objective 2 (Methodological challenges): Understand common methods for performing business analytics • Being able to name common methods for business analytics, as well as their underlying concepts • Being able to contrast supervised vs. unsupervised learning (clustering)

Content

Prior to the start of the Information Age in the late 20th century, companies back then lacked the computing capabilities necessary for data to be analyzed, and as a result, decisions primarily originated not from knowledge but from intuition. With the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on computer-aided “Business Analytics”. As examples, machine Learning algorithms enable detection of patterns and predict or recommend actions by processing large data sets of information, instead of response to instructions. Deep learning is a type of machine learning using Neural Networks to process huge amounts of data through successive layers of learning to arrive at a conclusion or recommendation. This highly interactive and application driven course will lay a foundation of understanding of these cutting-edge concepts, followed by a contemporary Case Study of relevance to marketplace application. The class dialog will bring out the underlying complexities of under-standing business challenges and determining the suitability of AI solutions thus enhancing participants AI/ML decision making. This will be followed by a participative discussion to connect the knowledge and case study application to the participants own experiences. Based on it, we jointly define the criteria for the type of situations where AI and ML are appropriate and develop potential solutions. Developing a technological solution to an AI challenge is only the first step. The practitioner will need to recognize implementation as a potentially disruptive change that will require careful change management leadership for effective implementation. Given the novelty of the theme and the rare experience in industry, this part will be accompanied by insights from practitioners.

Resources