Course ObjectiveAt 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
- Understand the consequences of overfitting and underfitting, and
demonstrate this knowledge by successfully preventing it in their own
- 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.
- 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.
- Acquiring knowledge and understanding of machine learning algorithms
independently, to such an extent that you can adequately explain it.
Course ContentThis 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 MethodsLectures and Guest-Lectures (2x week), Practical sessions (1x week).
Method of AssessmentThere 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 reading material will be provided through Canvas.
Target AudienceMinor Data Science
Minor Business Analytics & Data Science
Recommended background knowledgeKnowledge of statistical methods and Python is desirable.
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. D. Pierina Brustolin-Spagnuelo|
|Examiner||dr. D. Pierina Brustolin-Spagnuelo|
dr. E.M. Maassen MSc
dr. D. Pierina Brustolin-Spagnuelo
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Seminar, Lecture|
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