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 2024-2025. Please note that changes may occur.

Facts

Course code

MSB103

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

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
  • Panel regression
  • Math and stat review
  • and more

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

None

Recommended prerequisites

Appropriate bachelor background in core business fields as well as basic statistical knowledge.

Exam

Form of assessment Weight Duration Marks Aid
School exam (multiple choice) 1/1 3 Hours Letter grades

The evaluation for the course is based on an individual written exam. Students who do not pass the exam can take a deferred exam in a comparable format.

Coursework requirements

Assignments
Students must complete a share of the quizzes (as specified by the instructor) and participate in group projects to be eligible to take the written exam.

Course teacher(s)

Course coordinator:

Tom Brökel

Course teacher:

Tom Brökel

Study Program Director:

Yuko Onozaka

Method 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

Open for

Master of Science in Accounting and Auditing Business Administration - Master of Science

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto