Introduction to Data Science


Course Objective

At the end of the course the students will be able to:

Knowledge and Understanding:
- Explain the scope of the field of Data Science, and the phases that a
data science project goes through.
- Explain and apply feature detection and selection, labels, supervised
and unsupervised algorithms, big data and other concepts related to the
field of Data Science.
- Explain the pros and cons of the above methods for specific

Applying knowledge and understanding:
- Apply this knowledge by selecting the suitable methods for different
data sets.
- Understand the consequences of overfitting and underfitting, and
demonstrate this knowledge by successfully preventing it in their own

Making judgements:
- Evaluate your own choices in the data science project you will do.
- Explain ethical issues and dilemmas around data collection and usage,
and demonstrate this knowledge by taking them into account in your own
project, if applicable.
- Select and use suitable visualisation methods for presenting
conclusions of a given project.

Communication skills:
- Present and explain a machine learning algorithm.
- Present to peers about their approach and results of a data science
- Make an adequate and concise data science project proposal.
- Collaborating with peers in a data science project.

Learning skills:
- Acquiring knowledge and understanding of machine learning algorithms
independently, to such an extent that you can adequately explain it.

Course Content

This introductory course will provide an overview of the field of Data
Science, the different specialities within data science and the ethical
issues that arise around data collection and use. The student will
understand the daily activities of a data scientist, and get hands-on
experience with a first data science project. The lectures will be given
by many different people: scholars of different specialities, and people
who work in data science teams of big and small companies.

Teaching Methods

Lectures and Guest-Lectures (2x week), Practical sessions (1x week).

Method of Assessment

There will be ungraded assignments (pass/fail construction), one graded
group project, and one graded individual exam.

- Practical assignments in Python.
- Seminar on Machine Learning topics (Group assignment).
- Peer assessment of seminar presentations (Group assignment).
- For each lecture: formulation of one exam question with answers about
the lecture topic.

- Short essay on a possible data science project - 20%
- ‘Kickstarter’ Data Science Project (Group assignment) - 30%
- Exam (Individual) - 50%


- Grus, J.(2019). Data science from scratch: First principles with
Python. O'Reilly Media.

Additional materials:
- Additional reading material will be provided through Canvas.

Target Audience

Minor Data Science
Minor Business Analytics & Data Science

Recommended background knowledge

Knowledge of statistical methods and Python is desirable.

General Information

Course Code XB_0018
Credits 6 EC
Period P1
Course Level 300
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. D. Pierina Brustolin-Spagnuelo
Examiner dr. D. Pierina Brustolin-Spagnuelo
Teaching Staff dr. E.M. Maassen MSc
dr. D. Pierina Brustolin-Spagnuelo

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: