Course GIK2KM

Data Science & Machine Learning

7.5 Credits
First Cycle
To Syllabus

Starts week 13, 2024

The overall goal of the course is for students to acquire in depth knowledge and skills in the use and development of Data Science software as well as basic Data Science knowledge: i.e. an interdisciplinary approach to find, extract and discover patterns in data using methods of analysis, domain competence and technology.

Knowledge and understanding
Upon completion of this course, students will be able to:
  • Explain the data science life cycle
  • Explain Big Data and data analysis
  • Explain methods for data preparations

Skills and abilities
Upon completion of this course, students will be able to:

  • Apply unsupervised and supervised machine learning algorithms for problem-solving
  • Apply fundamental concepts in statistics and probability theory, including key concepts like probability distributions, statistical significance, hypothesis testing and regression
  • Use programming languages for data science/data analysis
  • Perform data extraction from text
  • Use exploratory data analysis (EDA) to describe the data using summary statistics and visualisation techniques

Values and attitudes
Upon completion of this course, students will be able to:
  • Interpret and analyse the results of a data extraction process, as well as evaluate the effects of choices made during the process
Starts and ends:
week 13, 2024 - week 22, 2024
Study Rate:
50%
Location:
Borlänge
Time of Day:
Day
Teaching form:
Normal
Language:
Swedish
Other:
P: Course only offered as part of programme.
Entry Qualifications :
  • Object-Oriented Programming 7.5 Credits, First cycle or other course in Fundamentals of Programming
  • Statistical Analysis 7.5 credits
Selection :
Guarenteed admission
Application Code:
HDA-V3GJK
Main field of study:
Tuition Fee
First Tuition Fee Installment:
16,875 SEK
Total Tuition Fee:
16,875 SEK
EU/EEA Citizens or exchange students are not required to pay fees.
Information on application and tuition fees: www.universityadmissions.se.
Closed for late application
Literature List

Literature lists are published at the latest one month ahead of the course start date.

To Literature List
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support@du.se
+46 23-77 88 88

Course room in Canvas

In the learning platform Canvas you can find more information about the course.

Visit the course room
Course Coordinator
Roger G Nyberg
Roger G Nyberg