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Data Analytics and Research Methods MSB112

"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”). To make sound, justifiable, and informed decisions, managers need to 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 the R software. Thereby, students will learn how to uncover the hidden patterns and narratives behind data in a scientific manner that will form the basis decision-makers in this data-driven world.


Course description for study year 2021-2022. Please note that changes may occur.

Fakta
Emnekode

MSB112

Vekting (SP)

20

Antall semestre

1

Vurderingssemester

Autumn

Undervisningsspråk

English

Tilbys av

UiS Business School

Learning outcome

Knowledge

On completion of the course, students will gain knowledge in:

  • Basic programming in R
  • Using R to analyze data and generate appealing presentations
  • Knowledge of data collection strategies and methods
  • Ethical considerations in collecting and working with empirical data
  • Constructing and estimating appropriate statistical models

 

Skills

Upon completion of this course, students will be able to:

  • Finding and accessing data
  • Building and managing empirical data sets for subsequent empirical analyses
  • Use R to construct a variety of measures, variables, and visualizations
  • Use R to analyze empirical data
  • Make proper interpretations of empirical findings
  • Presenting empirical analyses in an appealing and scientific manner
Content
Subject areas that are most likely covered are:
  • Introduction to R and basic programming
  • Data visualizations
  • Techniques of data gathering and processing
  • Ordinary Least Squares and diagnostics
  • Logistic regression and classification
  • Time series analysis
  • Data reduction techniques
Required prerequisite knowledge
None
Recommended prerequisites
Appropriate bachelor background in core business fields as well as mathematics for business/economic and statistics for business/economics.
Eksamen / vurdering

Individual exam and paper from group project

Vurderingsform Vekting Varighet Karakter Hjelpemiddel
Individual exam 1/2 3 Hours A - F
Paper from group project 1/2 A - F

The course has two parts and extents across two semesters. Each part has a specific assessment with students receiving a joint grade for the entire course. Specifically, the course is assessed based on two components. 50% of the final grade are based on an individual exam taking place at the end of the course’s first part (first semester). Students failing the exam will be able to re-take a deferred exam in the same format. The course’s second part concludes with a group project. The grade of the group project accounts for the remaining 50% of the grade of the course.

Coursework requirements
All assignments , Attendance in all group consultations with the supervisor, 1 Assignment (handing in of proposal)

First part: Students have to complete all assignments to be eligible for participation in the written exam.

Second part: Students are required to attend all scheduled group consultations with the supervisor to be eligible to handing in their group work.

Course teacher(s)
Course coordinator: Tom Brökel
Method of work

In this course, you will learn through the mixture of traditional lectures, exercises, seminars, group work and individual study. Lectures provide the basic theoretical knowledge behind the methods. Exercises and seminars are problem-based. Students will acquire practical knowledge of (1) basic programming and working with R; (2) handling of different datasets; (3) setting up problems and running appropriate statistical models; (4) properly interpreting empirical results. Students are required to obtain necessary knowledge by self-studying different materials including videos, textbook chapters, and lecture slides.

The two parts of the course differ in that the first part is centered on individual work, while the second part is organized as group work.

 

Expectations: 560 ECTS work hours divided between lectures, in-class and out-of-class (group) work, seminars, and independent study.

Open for
Business Administration - Master of Science
Course assessment
Students will have the opportunity to give feedback on the course first in an early dialogue, and in multiple course evaluations. 
Overlapping courses
Course Reduction (SP)
Data Analytics and Research Methods (MØA112) 20
Research Methods for Business Sciences (MØA103) 10
Data Analytics for Business Decisions (MØA104) 10
() 20
() 30
Literature
Search for literature in Leganto