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EBA 3530 Causality, Machine learning and Forecasting

EBA 3530 Causality, Machine learning and Forecasting

Course code: 
EBA 3530
Department: 
Data Science and Analytics
Credits: 
7.5
Course coordinator: 
Leif Anders Thorsrud
Rogelio Andrade Mancisidor
Course name in Norwegian: 
Causality, Machine learning and Forecasting
Product category: 
Bachelor
Portfolio: 
Bachelor of Data Science for Business - Programme Courses
Semester: 
2022 Spring
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
English
Course type: 
One semester
Introduction

This course provides a thorough introduction to two central problems in applied analytics: causal analysis on one side, and machine learning and forecasting techniques on the other. The aims of the two problems are complementary, and are here presented together to emphasize their differences and connections.

Learning outcomes - Knowledge

In this course, the student will:

  • Learn key Machine Learning algorithms used for prediction and classification.
  • Get a thorough introduction to various forecasting techniques and evaluation.
  • Understand the fundamental limitations of observational studies, and the types of assumptions that are required to use observational data to make causal (counter factual) statements.
  • Understand how experiments and quasi-experiments can be used to overcome these difficulties.
Learning outcomes - Skills

After finishing this course, the student will be able to:

  • Work with cross-sectional data, time series data, and text as data.
  • Apply and choose among fundamental forecasting and Machine Learning methods.
  • Apply regression techniques to analyze data from experiments and quasi-experiments.
General Competence

The students should be able to think critically about, and apply, Machine Learning techniques for forecasting and causal inference.  A successful candidate will be in a good position to conduct applied Data Science work, or expand his/her knowledge in more advanced courses on the topic.

Course content
  • Fundamental principles of statistical learning and forecasting techniques: bias/variance trade-off, cross validation techniques and pseudo out of sample methods.
  • The problems surrounding analysing causality through observational data.
  • Experiments and quasi-experiments.
  • Key Machine Learning algorithms, including, regression, time series processes, regularization, and classification, and their connection to the above issues.
Teaching and learning activities

Lectures and practical exercises that must be solved on the computer. Applied work will use Python/R.

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

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

EBA3500 Data Analysis with Programming, EBA 2904 Statistics, EBA 2910 Mathematics for Business Analytics, EBA 3400 Programming, data extraction and visualisation or equivalent courses.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 35301
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination every semester
25No1 Week(s)Group (2 - 3)Written home exam. Work in groups of up to three. Requires Python/R. All exam elements must be passed to achieve a final grade in the course.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 35302
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination every semester
75Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Written school exam. All exams must be passed to obtain a final grade in the course.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:25
Invigilation:No
Grouping (size):Group (2-3)
Support materials:
Duration:1 Week(s)
Comment:Written home exam. Work in groups of up to three. Requires Python/R. All exam elements must be passed to achieve a final grade in the course.
Exam code:EBA 35301
Grading scale:ECTS
Resit:Examination every semester
Exam category:Submission
Form of assessment:Written submission
Weight:75
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:Written school exam. All exams must be passed to obtain a final grade in the course.
Exam code:EBA 35302
Grading scale:ECTS
Resit:Examination every semester
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Seminar groups
9 Hour(s)
Student's own work with learning resources
130 Hour(s)
Group work / Assignments
25 Hour(s)
Sum workload: 
200

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.