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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 2021-2022

Course code




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Semester tution start


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Learning outcome


  • Execute 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 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 
  • Evalute, communicate and defend results of data analysis

General qualifications:

  • Solve real-world data analysis problems following a well-structured process
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 i.a. 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).
Required prerequisite knowledge
10 Credits in Programming, Databases or Software Engineering related courses.

Project work and oral presentation

Form of assessment Weight Duration Marks Aid
Oral presentation 40/100 A - F
Individual hand in 60/100 A - F

Project is completed in groups. Project work is to be performed at the times and 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.Both project and oral examination must be done before final grade is given. If a student fails the projectwork , he/she have to take this part again next time the subject is lectured.2 - 4 assignments completed individually. Both project and assignments must be passed to get the final grade in this course.

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 additional 6-8 hours a week on self-study, group discussions, and development work (open laboratory).
Open for
Applied Data Science, Master's degree Programme Computer Science - Master's Degree Programme Exchange programme at Faculty of Science and Technology
Course assessment
Form and/or discussions.
The syllabus can be found in Leganto