GRA 4110 Applied Data Analytics

GRA 4110 Applied Data Analytics

Course code: 
GRA 4110
Department: 
Economics
Credits: 
6
Course coordinator: 
Steffen Grønneberg
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2020 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

This course gives an applied introduction to the most important techniques in business-related data analytics. Students are given hands-on experience with programming, working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Programming, mathematical theory and applications will be interwoven in application focused projects.

Learning outcomes - Knowledge

Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system. Skills in working with high technical precision will be developed.

Learning outcomes - Skills

The student will be trained in the extremely flexible R system to do applied data analysis. This includes control-structures, loops, data importation and reorganization, the use of visualization techniques, and some general programming, as well as using descriptive statistics, calling upon and implementing some statistical procedures, as well as writing simple simulation experiments.

Skills in data-selection, data-reorganization, data-transformations and descriptive statistics will be developed in connection with data-visualization, model formulation, model diagnostics and model selection will be developed. Skills in choosing and using exploratory tools for getting an overview of large datasets will be developed. Finally, skills in turning a practical question into a question that can be addressed via statistical tools, and then using statistical tools to decide on a course of action for the practical question at hand will be developed.

Skills in technical and mathematical understanding will also be developed, mainly through work on mathematical problems related to the applied projects, focusing on the interplay between arithmetical rules, data access, and implementation of computer algorithms.

General Competence

Through experience in model building and computer experiments, the student will reflect on the limitations of statistical techniques, the issue of subjectivity in reaching statistical conclusions, and the level of trust one may place in statistically based decisions. Further, simulation and out of sample forecasts will be introduced in order to assess the validity and quality of a statistical technique.

Course content
  • Introduction to sums and summation notation, and other foundational issues.
  • Introduction to R. Introductory descriptive statistics, data visualization and data re-organization. Data exploration and visualization in R
  • A/B testing, and a review of statistical inference via testing for a proportion (exact theory), a brief review of large sample inference, and an introduction to the bootstrap
  • An introduction to data-modelling: Simple regression models and an introduction to simulation.
  • Multiple linear regression: Dummy-variables, interaction terms, data-transformations and interpretation.
  • Simulation and out of sample forecasts.
  • Regression diagnostics and model selection.

 

Teaching and learning activities

-

Software tools
R
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to get a grade in the course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Required prerequisite knowledge

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Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41101
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No1 Week(s)Group/Individual (1 - 3)Assignment An oral defense of the assignment might be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41102
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group/Individual (1-3)
Support materials:
Duration:1 Week(s)
Comment:Assignment An oral defense of the assignment might be required.
Exam code: GRA 41101
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:
Exam code: GRA 41102
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching on Campus
36 Hour(s)
Lectures, including time for project work (blended learning).
Teaching on Campus
12 Hour(s)
Project work under supervision, in class rooms
Examination
15 Hour(s)
Work with home exam
Examination
3 Hour(s)
Final exam
Student's own work with learning resources
94 Hour(s)
Sum workload: 
160

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.