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2019/2020 KAN-CDASO2040U Predictive Analytics
| English Title | |
| Predictive Analytics |
Course information |
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| Language | English |
| Course ECTS | 7.5 ECTS |
| Type | Mandatory offered as elective |
| Level | Full Degree Master |
| Duration | One Semester |
| Start time of the course | Spring |
| Timetable | Course schedule will be posted at calendar.cbs.dk |
| Study board |
Study Board for BSc/MSc in Business Administration and
Information Systems, MSc
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| Course coordinator | |
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| The teacher need not be the course responsible person. | |
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| Teaching methods | |
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| Last updated on 03-07-2019 | |
Relevant links |
| Learning objectives | ||||||||||||||||||||||
To achieve the grade of 12, students should meet
the following learning objectives only with no or minor mistakes or
errors. By the end of the course the students will be able to:
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| Course prerequisites | ||||||||||||||||||||||
| The course assumes familiarity with either a basic courses in statistics and regression analysis or a basic course in econometrics. Concepts such as random variables and familiarity with the principles of statistical hypotheses testing are assumed known. | ||||||||||||||||||||||
| Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||
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Number of compulsory
activities which must be approved: 2
Compulsory home
assignments
Each assignment is 1-3 pages in group of 1-4 students. The students have to get 2 out of 4 assignments approved in order to go to the exam. There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max. 10 pages) which will cover 2 mandatory assignments. |
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| Examination | ||||||||||||||||||||||
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| Course content, structure and pedagogical approach | ||||||||||||||||||||||
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Predictive analytics is a field that potentially can provide managers with very valuable tools for decision making. Both for sales variables of firms but also for financial variables like stock prices forecasting models and methods may be of interest, and during the course we will discuss challenges in relation to preparing relevant data for forecasting, choosing well-suited models for analysis and we offer a range of tools for evaluating the forecasting performance of the models we present. In one of the lectures we introduce a tool that can be very helpful for model selection if ‘Big Data’ is available. This course is designed to provide both a theoretical foundation for predictive modeling but also hand-on experience with analyzing economic data. The models presented in the course will belong to different fields of statistics but in all cases the predictive power of the models will be in focus. Introducing statistical tools allows us to not only come up with an actual forecast of an economic variable of interest (think e.g. of sales) but also to assess the uncertainty of the forecast. Before a proper model can be selected it is of interest to discuss issues about data availability and also data reliability. Such topics are often not very much discussed in the text book but providing the students with examples of the types of challenges one may encounter – based on the research of the teachers – will try to fill this gap. |
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| Description of the teaching methods | ||||||||||||||||||||||
| Lectures, in-class exercises during lectures, exercise classes with tools training. | ||||||||||||||||||||||
| Feedback during the teaching period | ||||||||||||||||||||||
| On a continuous basis in class. This holds true for both lectures and exercise classes. In addition the students are very welcome during the office hours of the teacher. During office hours students can receive feed-back both on their choice of data for the exam project, on technical issues in relation to the use of R in general (only to a limited degree for their exam project as no supervision is allowed. Minor questions can be answered if the teacher does not find it unfair to the other students). Also feed-back on theoretical questions is provided during office hours. Finally individual feed-back to students are provided during the assessment of the mandatory home assignments and indicative solutions will also be provided. | ||||||||||||||||||||||
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| Expected literature | ||||||||||||||||||||||
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The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.
Main text book(s) and articles: Hyndman, R.J & Athanasopoulos, G (2014): Forecasting: principles and Practice (HA)
Gujarati, D.N. and Porter, D.C. (2010). Essentials of Econometrics, 4th edition, McGraw-Hill, pp 201-205 and 386-397
Doornik, J.A. & Hendry, D.F. (2014): Statistical model selection with ‘Big Data’. University of Oxford, Department of Economics, Discussion Paper Series, # 735 (or more recent text in this spirit). (DH)
More supplementary stuff: Buus Lassen, N., la Cour, L., Vatrapu, R. (2017), ‘Predictive Analytics with Social Media data’ in Sloan & Quan-Haase ed. The SAGE Handbook of Social Media Research Methods, Chapter 20, pp 328-341 (BCV)
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