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EBA 3501 Foundations of Data Science

EBA 3501 Foundations of Data Science

Course code: 
EBA 3501
Data Science and Analytics
Course coordinator: 
Jonas Moss
Course name in Norwegian: 
Foundations of Data Science
Product category: 
Bachelor of Data Science for Business - Programme Courses
2024 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

In this course, we will embark on an exciting journey into the realm of machine learning and data science. Machine learning, a subset of AI, empowers computers to learn from data and make predictions or decisions without explicit programming.

Throughout this course, we will leverage the power of Python, the most popular language for machine learning. Libraries such as scikit-learn provides a rich set of tools for data preprocessing, model building, evaluation, and much more.

This course will equip you with the foundational knowledge and practical skills needed to tackle real-world problems involving data analysis, classification, and regression.

Learning outcomes - Knowledge

Upon completion of this course, students will be able to:

  • Understand the basic concepts of machine learning and exploratory data analysis.
  • Know the most popular machine learning methods' definition, interpretation, and properties.
  • Appreciate the reasoning behind basic workflows in data science.
Learning outcomes - Skills

Upon completion of this course, students will be able to:

  • Apply Python libraries for loading, cleaning, and exploration of data.
  • Fit a variety of important machine learning models in Python and interpret the results.
  • Present data analyses professionally using Quarto and Jupyter notebooks.
General Competence

Upon completion of the course, students will have stronger competence in:

  • Work on difficult problems, independently and in teams.
  • Read and understand technical documentation.
  • Present analyses professionally.
Course content

The course covers the following topics:

  • Loading, cleaning, and exploration of data.
  • Quarto and Jupyter notebooks for the presentation of exploratory data analyses and applications of machine learning methods.
  • Fitting of basic classifiers.
  • Run and make informed choices among linear regression.
  • Interpret the coefficients of regression models along with their p-values.
  • Splitting data into test and training sets.
  • Construction estimator pipelines, including data loading, preprocessing, fitting, and model evaluation.
  • Perform regularized regression, such as ridge and lasso.
  • Usage of non-linear features such as polynomials and splines.
  • Feature transformations such as one-hot encoding.
  • Make informed choices between different models using cross-validation.
Teaching and learning activities

The course uses the following methods for teaching and learning:

The course will be a combination of lectures and tutorials. 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 Itslearning or in the text book.

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

Higher Education Entrance Qualification


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

EBA3400 Programming, data extraction and visualisation, EBA 1180 Mathematics for Data Science or equivalent courses.

Exam category: 
Form of assessment: 
Submission other than PDF
Exam/hand-in semester: 
First Semester
Group/Individual (1 - 3)
1 Week(s)
Home exam.
Exam code: 
EBA 35011
Grading scale: 
Examination every semester
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Prepare for teaching
45 Hour(s)
45 Hour(s)
25 Hour(s)
Student's own work with learning resources
85 Hour(s)
Sum workload: 

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.