ID2214 Programming for Data Science 7.5 credits
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.
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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.
Course disposition
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
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
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
Opportunity to raise an approved grade via renewed examination
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 web
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web ID2214Offered by
Main field of study
Education cycle
Add-on studies
Contact
Supplementary information
In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.