Introduction to Data Science (DAT540)

The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.

Course description for study year 2023-2024. Please note that changes may occur.


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The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors, and industries. In this course, students become familiar with basic tools and processes used in Data Science. Students work through the whole data lifecycle from loading, through cleaning and modeling, to storing the data. The work is performed using Python stack consisting ia of: IPython, NumPy, Pandas, Matplotlib, and Jupyter Notebooks. Students learn to structure their work using CRISP-DM and Data Science Process (Ask, Get, Explore, Model, Communicate and Visualize).

Learning outcome

Knowledge :

  • Execute/Develop tools to load, parse, clean, transform, merge, reshape, and store data.
  • Compare regular Python, NumPy, and Pandas data structures and choose one for the given problem. Use the IPython shell and Jupyter notebook for exploratory computing.
  • Execute/Develop simple machine learning or data mining algorithms.


  • Organize data analysis following CRiSP-DM and Data Science Process
  • Build engaging visualizations of data analysis using matplotlib
  • Optimize data analysis applying available structure and methods
  • Evaluate, communicate and defend results of data analysis

General qualifications :

  • Solve real-world data analysis problems following a well-structured process

Required prerequisite knowledge

10 Credits in Programming, Databases or Software Engineering related courses.

Recommended prerequisites

DAT120 Introduction to Programming, STA500 Probability and Statistics 2


Project work and written exam

Form of assessment Weight Duration Marks Aid
Project work in groups 3/5 Letter grades
Written exam (Multiple Choice) 2/5 3 Hours Letter grades

Project Work in GroupsThe project is completed in groups. Project work is to be performed in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the lecturer.A project report, including source code, contributes to the grade. If a student fails the project work, he/she has to take this part again the next time the subject is lectured.

Course teacher(s)

Course coordinator:

Antorweep Chakravorty

Head of Department:

Tom Ryen

Method of work

The work will consist of 6 hours of lecture, scheduled laboratory, supervised group work per week. Students are expected to spend an additional 6-8 hours a week on self-study, group discussions, and development work.

Open for

Data Science - Master of Science Degree Programme Computer Science - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto