GRA 6020 Applied Data Analytics

GRA 6020 Applied Data Analytics

Course code: 
GRA 6020
Department: 
Economics
Credits: 
6
Course coordinator: 
Steffen Grønneberg
David Kreiberg
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2019 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

This course gives an introduction to some of the more important tools and techniques used in data analytics for business. Students will obtain hands-on experience working with real data problems, they will learn how to use descriptive statistics to explore data and justify models, and how to use statistical models to turn data into actionable knowledge.

Learning outcomes - Knowledge

Basic knowledge of formal statistical methodology will be reviewed and applied. Focus will be on various statistical modeling frameworks typically applied in the social sciences. The students will learn how to perform and interpret statistical tests, make confidence intervals, and will acquire understanding and knowledge of the basic theory and motivation underlying and associated with regression type models.

Learning outcomes - Skills

The course focuses on practical data analytics, thereby empowering the student to do independent data analyses on their own.

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.

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. The use of statistical software, such as Excel, R, SPSS and Stata, is an essential element in modern statistical analysis. Skills in the practical use of such programs will therefore be developed.

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.

Course content
  1. Introductory descriptive statistics, data visualization and data re-organization.
  2. Data exploration.
  3. Introductory statistical inference.
  4. An introduction to various statistical modeling frameworks, such as multiple linear regression analysis (including ANOVA), factor analysis and other multivariate modeling techniques of interest.
  5. Diagnostics and model selection.
Teaching and learning activities

One of several of the software tools mentioned are relevant for the course.

Software tools
Software defined under the section "Teaching and learning activities".
R
SPSS
Stata
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.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 60207
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No 14 Day(s)Group (1 - 3).
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 60208
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60Yes3 Hour(s)
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Written examination under supervision.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (1-3)
Support materials:
Duration: 14 Day(s)
Comment:.
Exam code:GRA 60207
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:
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment: Written examination under supervision.
Exam code:GRA 60208
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100