365-1120-00L Executive Business Analytics
| Semester | Spring Semester 2020 |
| Lecturers | S. Feuerriegel |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Exclusively for MAS MTEC students (2nd semester). |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 365-1120-00 G | Executive Business Analytics One-day course: 6 March 2020. Friday: 08:00-17:00. Please note: The kick-off event is organized as a 1-day lecture. The predominant work goes into the (group) projects that are to be developed throughout the semester (details follow in the first lecture). This is accompanied by additional coaching sessions (optional) where students can sign-up on a rolling basis during the semester. | 8s hrs |
| S. Feuerriegel |
Catalogue data
| 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. |
| Learning 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. |
| Lecture notes | The following technical aspects will be covered from a methodological angle: - Forecasting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured data to facilitate the predictive performance? What are metrics to evaluate the performance of predictions? - Data analysis: How can one derive explanatory power in order to study the response to an input? - Clustering: How can businesses group consumers into distinct categories according to their purchase behavior? - Dimension reduction: How can businesses simplify a large amount of indicators into a smaller subset with similar characteristics? |
| Literature | The course involves two pre-readings that students are kindly asked to read before the first class: Reading 1 DeepMind creates algorithm to predict kidney damage in advance https://on.ft.com/332Cx6V Reading 2 Building the AI-Powered Organization https://hbr.org/2019/07/building-the-ai-powered-organization |
| Prerequisites / Notice | Students, who have already successfully completed the course “Business Analytics (363-1098-00)” can't register again. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 1 credit |
| Examiners | S. Feuerriegel |
| Type | ungraded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Additional information on mode of examination | Credit points will only be granted if the following criteria are met: There is a presentation and project report. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| Places | 77 at the most |
| Priority | Registration for the course unit is only possible for the primary target group |
| Primary target group | MAS ETH in Management, Technology, and Economics (365000) starting semester 02 |
| Waiting list | until 09.02.2020 |
| End of registration period | Registration only possible until 26.01.2020 |
Offered in
| Programme | Section | Type | |
|---|---|---|---|
| MAS in Management, Technology, and Economics | Quantitative and Qualitative Methods for Solving Complex Problems | W |  |