Data Analytics (MSB103)
"There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT). Over the last two years alone 90 percent of the data in the world was generated." (Marr, 2018). In today’s knowledge economy, data is frequently seen as the most crucial resource ("the new oil"). In order to make sound, justifiable, and informed decisions, managers must be able to quickly access, process, and analyze up-to-date information on a small- and large-scale basis. This course offers training for such knowledge and skills, through the use of statistical models, real data from around the world, and R software. Thus, students will learn how to uncover the hidden patterns and narratives behind data in a scientific manner that will form the basis for decision-makers in this data-driven world.
Course description for study year 2023-2024. Please note that changes may occur.
Course code
MSB103
Version
1
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Content
Subject areas that are most likely covered are:
- Introduction to R and basic programming
- Data visualizations
- Ordinary least squares and diagnostics
- Logistic regression and classification
- Time series analysis
- Data reduction techniques
- Panel regression
- Math and stat review
Learning outcome
Knowledge
On completion of the course, students will have gained knowledge of:
- Basic programming in R
- Using R to analyze data and generate attractive presentations
- Constructing and estimating appropriate statistical models
Skills
Upon completion of this course, students will be able to:
- Use R to construct a variety of measures, variables, and visualizations and analyze empirical data
- Assess and employ basic multivariate statistical models
- Evaluate and interpret statistical results of basic multivariate statistical models
Required prerequisite knowledge
Recommended prerequisites
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| School exam (multiple choice) | 1/1 | 3 Hours | Letter grades | Open book 1) |
1) Open book exam
The course is assessed based on an individual exam taking place at the end of the semester. Students who do not pass the exam will be able to take a deferred exam in the same format.
Coursework requirements
Course teacher(s)
Course coordinator:
Tom BrökelStudy Program Director:
Yuko OnozakaMethod of work
In this course, you will learn through a combination of traditional lectures, exercises and individual study. Lectures provide the basic theoretical knowledge behind the methods. Students will acquire practical knowledge of (1) basic programming and working with R; (2) handling different datasets; (3) setting up problems and running appropriate statistical models; (4) properly interpreting empirical results. Students are required to obtain the necessary knowledge through self-study of different materials including videos, textbook chapters and lecture slides.
Expectations: 280 ECTS hours divided between lectures, in-class and out-of-class (group) work, seminars and independent study.
Overlapping courses
| Course | Reduction (SP) |
|---|---|
| Data Analytics for Business Decisions (MØA104_1) | 10 |
| Data Analytics for Business Decisions, Data Analytics and Research Methods ( MØA104_1 MØA112_1 ) | 20 |
| Data Analytics and Research Methods (MØA112_1) | 10 |
| Data Analytics and Research Methods (MSB112_1) | 10 |
| Data Analytics and Research Methods, Data Analytics for Business Decisions ( MSB112_1 MØA104_1 ) | 20 |
| Data Analytics and Research Methods, Data Analytics and Research Methods ( MSB112_1 MØA112_1 ) | 30 |
| Data Analytics and Research Methods, Data Analytics for Business Decisions, Data Analytics and Research Methods ( MSB112_1 MØA104_1 MØA112_1 ) | 40 |