Skip to main content
Till KTH:s startsida

FID3214 Programming for Data Science 7.5 credits

Having passed the course, the student should be able to account for, discuss, implement and apply I) techniques to convert data to appropriate format for data analysis, II) algorithms to analyse data through supervised and unsupervised machine learning as well as III) techniques and performance measurements for evaluation of data analysis results.

Information per course offering

Termin

Information for Autumn 2024 Start 28 Oct 2024 programme students

Course location

KTH Kista

Duration
28 Oct 2024 - 13 Jan 2025
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

50941

Form of study

Distance Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
[object Object]
Schedule
Schedule is not published
Part of programme
No information inserted

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted
Contact

Henrik Boström (bostromh@kth.se), Amirhossein Akhavanrahnama (amiakh@kth.se)

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus FID3214 (Autumn 2020–)
Headings with content from the Course syllabus FID3214 (Autumn 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Syntax and semantics for programming languages that are particularly suited for data science, e.g., Python. Routines to import, combine, convert and make selection of data. Algorithms for handling of missing values, discretisation and dimensionality reduction. Algorithms for supervised machine learning, e g naive Bayes, decision trees, and random forests. Algorithms for unsupervised machine learning e g k-means clustering. Libraries for data analysis. Evaluation methods and performance metrics. Visualisation and analysis of results of data analysis. 

Course structure

Ten lectures (non-mandatory)

One mandatory seminar

Four assignments, of which one is to be presented at the seminar

Course literature

I. Witten, E. Frank, M. Hall and C. Pal, Data Mining: Practical Machine Learning Tools and Techniques (4th ed.), Morgan Kaufmann, 2016 ISBN: 9780128042915. J. VanderPlas, Python Data Science Handbook: Essential tools for working with data (1st ed.), O'Reilly Media Inc., 2016 ISBN: 9781491912058.

Required equipment

Own computer

Intended learning outcomes

Having passed the course, the student should be able to:

1. account for and discuss the application of

  • techniques to convert data to appropriate format for data analysis
  • algorithms to analyse data through supervised and unsupervised machine learning, as well as
  • techniques and performance measurements for evaluation of data analysis results

2. implement and apply

  • techniques to convert data to an appropriate format for data analysis
  • algorithms for supervised and unsupervised machine learning, as well as
  • techniques and performance measurements for evaluation of data analysis results

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

  • EXA1 - Examination, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Three of the four assignments are the same as for the corresponding course on the advanced level (ID2214). The fourth assignment consists of a small-scale research project, in which a research question has to be identified and results have to be presented in a short scientific article.

Other requirements for final grade

Pass on all assignments, including presentation at the final seminar. 

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Contact

Henrik Boström (bostromh@kth.se), Amirhossein Akhavanrahnama (amiakh@kth.se)

Postgraduate course

Postgraduate courses at EECS/Software and Computer Systems