Du hittar kurs-PM för nyare kursomgångar på sidan Kurs-PM.
Course presentation
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.
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2020
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.
Preparations before course start
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. Available online for free here (Links to an external site.).
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Please inform the course coordinator if you need compensatory support during the course. Present a certificate from Funka.
Examination and completion
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 raise an approved grade via renewed examination
There is no possibility to raise an approved grade by renewed examination.
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
No information inserted
Contacts
Communication during course
Questions from course participants should primarily be submitted to the appropriate discussion group in Canvas, or if there is no such relevant group, directly to the course coordinator in Canvas.
Questions from course participants should primarily be submitted to the appropriate discussion group in Canvas, or if there is no such relevant group, directly to the course coordinator in Canvas.
