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ID2214 Programming for Data Science 7.5 credits

Administer About course

The course covers the following topics:

  • Syntax and semantics for programming languages that are particularly suited for data science, e.g., Python.
  • Routines for importing, combining, transforming and selecting data.
  • Algorithms for handling missing values, discretisation and dimensionality reduction.
  • Algorithms for supervised machine learning, e.g., naïve Bayes, decision trees, random forests.
  • Algorithms for unsupervised machine learning, e.g., k-means clustering.
  • Libraries for data analysis.
  • Evaluation methods and performance metrics.
  • Visualising and analysing results.

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Application code

50125

Headings with content from the Course syllabus ID2214 (Autumn 2021–) 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., naïve Bayes, decision trees, 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.

Intended learning outcomes

Having passed the course, the student should be able to

  • account for and discuss the application of i) technologies to convert data to an appropriate format for data analysis ii) algorithms to analyse data through supervised and unsupervised machine learning as well as iii) technologies and performance metrics for evaluation of data analysis results
  • implement and apply i) technologies to convert data to an appropriate format for data analysis ii) algorithms for supervised and unsupervised machine learning as well as iii) technologies and performance metrics for evaluation of data analysis results.

Literature and preparations

Specific prerequisites

Completed course in programming equivalent to ID1018/DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1331/DD1337/DD100N.

Active participation in a course offering where the final examination is not yet reported in Ladok is considered equivalent to completion of the course.

Registering for a course is counted as active participation.

The term 'final examination' encompasses both the regular examination and the first re-examination.

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

A, B, C, D, E, FX, F

Examination

  • INL1 - Assignment, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Examination, 3.0 credits, grading scale: A, B, C, D, E, FX, 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.

Written examination. Written assignments.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Henrik Boström

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

EECS/Computer Science

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

Henrik Boström, bostromh@kth.se

Supplementary information

In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.