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# EBA 3500 Data Analysis with Programming

## EBA 3500 Data Analysis with Programming

Course code:
EBA 3500
Department:
Economics
Credits:
7.5
Course coordinator:
Steffen Grønneberg
Course name in Norwegian:
Data Analysis with Programming
Product category:
Bachelor
Portfolio:
Bachelor of Business Analytics - Programme Courses
Semester:
2021 Autumn
Active status:
Active
Level of study:
Bachelor
Teaching language:
English
Course type:
One semester
Introduction

The students will learn the most important techniques in applied statistics and data analysis, with an emphasis on linear regression. Students are given hands-on experience with data analysis projects, and will gain further knowledge in working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Data examples and applications will be given.

Learning outcomes - Knowledge

After completing the course, the student should know:

• What a statistical model is, and how they may be of use in prediction, modelling, and explanations.
• The basic mathematical underpinnings of the linear regression model.
• The scope and limitations of a multiple linear regression model.
• That how a dataset is gathered (e.g. sampling design) and our degree of substantial knowledge of the data drives how strong statements we may make using statistical tools.
• Some of the most important practical limitations in using the multiple linear regression framework.
• The most common sets of assumptions underlying consistency, as well as exact or approximate normality for parameter estimates in linear regression.
• Know the basic interpretation of a multiple linear regression model, and when this may break down.
Learning outcomes - Skills

After completing the course, the students will:

• Possess basic skills in data-selection, data-reorganization, data-transformations.
• Develop further skills in descriptive statistics and data-visualization.
• Be able to use descriptive and transformative techniques to formulate a reasonable multiple linear regression model for a dataset.
• Develop basic and hand-on experience with model diagnostics, as well as having basic skills in choosing between competing models.
• Choosing appropriate exploratory tools for getting an overview of large datasets.
• Be able to turn a practical question into a question that can be addressed with multiple linear regression.
• Be able to use statistical tools to decide on a course of action for a given practical problem.
• Further develop programming skills in Python.
• Perform simulation studies to assess how well a statistical method performs.
General Competence

Students will understand that in many situations a statistical analysis will help making better decisions, but also be aware that statistical methods can be wrongly applied and lead to false conclusions.

Course content

The following topics will be covered using Python as statistical analysis system.

• Statistical inference for simple linear regression and basic
residual analysis.
• Briefly on the multiple linear regression model and general OLS. Residuals.
• Linear regression with a linear and quadratic term: Introduction to
multiple linear regression and OLS.
• Multiple linear regression with categorical variables: OLS estimation, ANOVA and the comparison of group averages. Introduction to the F-test.
• Introduction to multiple linear regression modelling: Assumptions, inference, diagnostics and influential observations.
Teaching and learning activities

Teaching will done using a blend of lectures and applied data projects in Python.

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

.

Qualifications

Higher Education Entrance Qualification

### Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

### Teaching

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

EBA3400 Programming, data extraction and visualisation, EBA 2910 Mathematics for Business Analytics or equivalent courses.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 35001
ECTS
Internal and external examiner
Resit:
Examination every semester
40No1 Week(s)Group/Individual (1 - 3)Home exam. An oral defence may be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 35002
ECTS
Internal and external examiner
Resit:
Examination every semester
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: Home exam. An oral defence may be required. Exam code: EBA 35001 Grading scale: ECTS Resit: Examination every semester
 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: EBA 35002 Grading scale: ECTS Resit: Examination every semester
Type of Assessment:
Ordinary examination
Total weight:
100
ActivityDurationComment
Teaching
36 Hour(s)
Lectures and blended learning with projects for students.
Feedback activities and counselling
9 Hour(s)
Project-work under supervision.
Examination
15 Hour(s)
Work related to the home exam.
Student's own work with learning resources
75 Hour(s)
Group work / Assignments
62 Hour(s)
Examination
3 Hour(s)
Final exam