PTMBA 2020 The Analytics Edge
In this course, we shall explore techniques for descriptive, predictive and prescriptive analytics. This will enable better decision making in the presence of big data.
Course code
MBA SCM641, MBA SCM641 SI
Course type
PT MBA Lecture
Weekly Hours
2,0
ECTS
2.0
Term
HS 2019
Language
Englisch
Lecturers
Prof. Dr. Stefan Spinler
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
Course content
Today’s business world is characterized by an unprecedented growth of data, by 2020 we will experience a300‐fold increase from 2005. This data comes in a broad variety of forms: 420 million wearable healthmonitors are currently in use, more than 4 billion hours of video are watched on YouTube each month and
30 billion pieces of content are shared on Facebook every month. A lot of the data is analyzed in real time:modern cars have about 100 sensors and the NYSE captures 1 TB of trade information during each tradingsession. However, 1 in 3 business leaders don’t trust the information they use to make decisions and about
27% of respondents in one survey were unsure of how much of their data was inaccurate. 4.4 million ITjobs have been created globally to support big data. AlphaGo has recently beaten the reigning (human) Gochampion.
In this big data landscape, it becomes imperative for managers to understand what tools are available togather data, to subsequently aggregate data into information and how to use this information to make betterdecisions. To this end, we discuss and apply approaches to predictive analytics, which refers to the ability of
discerning patterns from past and current data to prediction of future events. Examples include healthcareapplications such as heart attack pattern detection and financial applications such as fraud detection inregards to credit card usage. An important step in the data analytics journey is the communication of results
which can be achieved via appropriate visualization and reporting.
The primary focus of this course is to gain an understanding of the potential of statistical and machinelearning approaches in business. Managers should have trust in their analytics teams that they come upwith solutions that entail a competitive edge – such trust will be bolstered by having first‐hand experiencewith analytical tools and the decisions based on them.
30 billion pieces of content are shared on Facebook every month. A lot of the data is analyzed in real time:modern cars have about 100 sensors and the NYSE captures 1 TB of trade information during each tradingsession. However, 1 in 3 business leaders don’t trust the information they use to make decisions and about
27% of respondents in one survey were unsure of how much of their data was inaccurate. 4.4 million ITjobs have been created globally to support big data. AlphaGo has recently beaten the reigning (human) Gochampion.
In this big data landscape, it becomes imperative for managers to understand what tools are available togather data, to subsequently aggregate data into information and how to use this information to make betterdecisions. To this end, we discuss and apply approaches to predictive analytics, which refers to the ability of
discerning patterns from past and current data to prediction of future events. Examples include healthcareapplications such as heart attack pattern detection and financial applications such as fraud detection inregards to credit card usage. An important step in the data analytics journey is the communication of results
which can be achieved via appropriate visualization and reporting.
The primary focus of this course is to gain an understanding of the potential of statistical and machinelearning approaches in business. Managers should have trust in their analytics teams that they come upwith solutions that entail a competitive edge – such trust will be bolstered by having first‐hand experiencewith analytical tools and the decisions based on them.
Class dates
| Date | Time |
|---|---|
| Sunday, 27.10.2019 | 09:15 - 16:45 |
| Saturday, 09.11.2019 | 09:15 - 16:45 |
| Sunday, 10.11.2019 | 09:15 - 16:45 |
| Sunday, 24.11.2019 | 23:50 - 23:55 |
Learning outcomes
literacy in data modelling and machine learning techniques
Literature
D. Bertsimas, A.K. O’Hair, W. R. Pulleyblank: The analytics edge. Dynamic ideas, 2016.
Learningmethods
Projects related to real data sets, exercises, using R for data analysis
Exam
Each session will contain short exercises related to descriptive and predictive analytics. There is oneintermediate assignment which counts 40%. To be done in teams.
The final project to be done on an individual basis, will allow students to work with a range of data sets to explore various techniques for model building; model analysis, interpretation of results and recommendations. The final project will make up 60% of the grade. The project’s report is to be delivered in R markdown.
Requirements
none