GRA 6020 Applied Data Analytics

GRA 6020 Applied Data Analytics

Course code: 
GRA 6020
Department: 
Economics
Credits: 
6
Course coordinator: 
Steffen Grønneberg
Course name in Norwegian: 
Applied Data Analytics
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2017 Autumn
Active status: 
Active
Level of study: 
Master
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 in various statistical models with focus on regression models. 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 Excel, R and modern statistical software will be used throughout the course. Skills in the practical use of such programs will therefore be developed.

Learning Outcome - Reflection

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. Introductory statistical inference.
  2. Data exploration.
  3. An introduction to data-modelling: Simple regression models.
  4. Multiple linear regression: Dummy-variables, interaction terms, data-transformations and interpretation.
  5. Regression diagnostics and model selection.
Learning process and requirements to students

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 that is not included on It's learning or text book.

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

Software tools
R
Additional information

Computer tools: Excel, R and related tools. 

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:
GRA60207
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No 14 Day(s)Group ( 2 - 3)Assignment/Term paper For students who attended GRA 6020 in autumn 2016 or earlier, an extraordinary resit examination will be arranged in autumn 2017. This resit examination will consist of GRA 60205 75% take-home exam (72 hours) and GRA 60206 25% multiple choice examination (2 hours).
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA60208
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 Final written examination with supervision For students who attended GRA 6020 in autumn 2016 or earlier, an extraordinary resit examination will be arranged in autumn 2017. This resit examination will consist of GRA 60205 75% take-home exam (72 hours) and GRA 60206 25% multiple choice examination (2 hours).
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (2-3)
Support materials:
Duration: 14 Day(s)
Comment:Assignment/Term paper For students who attended GRA 6020 in autumn 2016 or earlier, an extraordinary resit examination will be arranged in autumn 2017. This resit examination will consist of GRA 60205 75% take-home exam (72 hours) and GRA 60206 25% multiple choice examination (2 hours).
Exam code:GRA60207
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:Final written examination with supervision For students who attended GRA 6020 in autumn 2016 or earlier, an extraordinary resit examination will be arranged in autumn 2017. This resit examination will consist of GRA 60205 75% take-home exam (72 hours) and GRA 60206 25% multiple choice examination (2 hours).
Exam code:GRA60208
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

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.