Back to selection
10616-01 - Lecture: Applied Data Analysis (3 CP)
| Semester | fall semester 2016 |
| Course frequency | Irregular |
| Lecturers | Dietmar Maringer (dietmar.maringer@unibas.ch, Assessor) |
| Content | To counter-act the "data-rich, information-poor" ("drip") syndrome, this course covers concepts and techniques that aim at explorative analysis: finding structure within data, and, ideally, extracting information. These methods have their origins in areas such as machine learning, statistical learning, or data mining. These include (but are not limited to) non-linear regression methods, perceptrons and neural networks, support vector machines, tree-based methods, kernel-based methods, or rule-based methods. Typical applications are classification, prediction, clustering, or dimension reduction. Theoretical presentations are complemented with hands-on examples using the software package R and relevant toolboxes. Furthermore, we will discuss issues such as data preprocessing and data management. |
| Bibliography | T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer 2009. E. Alpaydin, Introduction to Machine Learning, 2nd ed., MIT Press 2010. I.H. Witten, E. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Elsevier 2011. additional literature will be announced in the lectures |
| Weblink | Weblink on ADAM |
| Admission requirements | completed BA in Business and Economics and following lecture: 12036 Econometrics |
| Course application | Course registration: please enrol personally with Prof. Maringer. Registration = Admission to the exam |
| Language of instruction | English |
| Use of digital media | Online, optional |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|
No dates available. Please contact the lecturer.
| Modules |
Statistics and Computational Science Module (Master Actuarial Science) Vertiefungsmodul Marketing and Strategic Management (Master Business and Economics) Vertiefungsmodul Quantitative Methods (Master Business and Economics) |
| Assessment format | end-of-semester examination |
| Assessment details | active participation and engagement during the lectures, written exam: date to be announced |
| Assessment registration/deregistration | Registration/deregistration: teaching staff |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |