Course

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.


Dette er emnebeskrivelsen for studieåret 2023-2024. Merk at det kan komme endringer.

See course description and exam/assesment information for this semester (2024-2025)

Semesters

Fakta

Emnekode

MSB103

Vekting (stp)

10

Semester undervisningsstart

Autumn

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Autumn

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

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

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

Appropriate bachelor background in core business fields as well as mathematics for business/economic and statistics for business/economics.

Exam

Form of assessment Weight Duration Marks Aid Exam system Withdrawal deadline Exam date
School exam (multiple choice) 1/1 3 Hours Letter grades Open book 1) Inspera assessment 14.11.2023 28.11.2023

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.

Vilkår for å gå opp til eksamen/vurdering

Assignments
Students must complete all assignments and participate in their group project (preparation and presentation) in order to be eligible to take the written exam.

Fagperson(er)

Study Program Director:

Ingeborg Foldøy Solli

Course coordinator:

Tom Brökel

Course teacher:

Tom Brökel

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

Emne Reduksjon (SP)
Data Analytics (MSB103_1) , Data Analytics for Business Decisions (MØA104_1) 10
Data Analytics (MSB103_1) , Data Analytics for Business Decisions (MØA104_1) , Data Analytics and Research Methods (MØA112_1) 20
Data Analytics (MSB103_1) , Data Analytics and Research Methods (MØA112_1) 10
Data Analytics (MSB103_1) , Data Analytics and Research Methods (MSB112_1) 10
Data Analytics (MSB103_1) , Data Analytics and Research Methods (MSB112_1) , Data Analytics for Business Decisions (MØA104_1) 20
Data Analytics (MSB103_1) , Data Analytics and Research Methods (MSB112_1) , Data Analytics and Research Methods (MØA112_1) 30
Data Analytics (MSB103_1) , Data Analytics and Research Methods (MSB112_1) , Data Analytics for Business Decisions (MØA104_1) , Data Analytics and Research Methods (MØA112_1) 40

Åpent for

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

Emneevaluering

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.

Kontakt

Study Program Director:

Ingeborg Foldøy Solli

Course coordinator:

Tom Brökel

Course teacher:

Tom Brökel

Tilbys av

UiS Business School

UiS School of Business and Law

The course description is retrieved from FS (Felles studentsystem). Version 1