The course is included in these curricula and study modules
- Information technology 2014 - Service oriented architectures and system design
- Information technology 2015 - Service oriented architectures and system design
- Information technology 2016 - Service oriented architectures and system design
- Information technology 2017 - Service oriented architectures and system design
- Information technology 2018 - Data processing and applied mathematics & physics
- Information technology 2019 - Data processing and applied mathematics & physics
The course takes place in period
2 (2020-10-26 to 2020-12-31)
Level/category
Teaching language
Swedish
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
3
Total number of ECTS
5 cr
Competency aims
The aim of the course is to introduce core
concepts
in data science, and familiarise students with
the
practicalities of working with various kinds of
data.
Learning outcomes
At the end of the course the student is
expected
to be able to
- Acquire, format, and visualize data using
Python and Pandas
- Be proficient with handling data of various
types
- Conduct web scraping and work with data APIs
- Understand signal processing and time-series
data analysis
- Analyse image data and social network graphs
Course contents
- Python and Pandas
- Visualisation
- Time series
- Images as data
- Graphs
Prerequisites and co-requisites
Good programming skills
Recommended or required reading
Recommended:
- "Python for Data Analysis", Wes McKinney
(O?Reilly
Media)
- Pandas documentation
http://pandas.pydata.org/ External link
Study activities
- Lectures - 36 hours
- Project- and production work/artistic activities - 37 hours
- Individual studies - 20 hours
- Internet-based studies - 10 hours
Workload
- Total workload of the course: 103 hours
- Of which autonomous studies: 103 hours
- Of which scheduled studies: 0 hours
Mode of Delivery
Participation in tuition
Assessment methods
Assessment requirements
To pass the course students should complete weekly
homework assignments (late delivery will reduce
grade)
Teacher
- Biström Dennis
- Karlsson Jonny
Examiner
Biström Dennis
Home page of the course
Group size
No limit (42 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2020-10-12 to 2020-11-08
Assessment methods
Date will be announced later - Other assignments
Date | Time | Room | Title | Description | Organizer |
---|---|---|---|---|---|
2020-10-29 | 12:00 - 15:45 | F365 | Databearbetning | https://arcada.zoom.us/my/bistromd | Biström Dennis |
2020-11-03 | 12:15 - 15:45 | F365 | Databearbetning | Biström Dennis | |
2020-11-10 | 12:15 - 15:45 | F365 | Databearbetning | Biström Dennis | |
2020-11-17 | 12:15 - 15:45 | F365 | Databearbetning | Biström Dennis | |
2020-11-20 | 10:00 - 13:30 | F365 | Databearbetning | Biström Dennis | |
2020-11-27 | 11:45 - 15:30 | F365 | Databearbetning | Biström Dennis | |
2020-12-03 | 12:15 - 16:00 | F365 | Databearbetning | Biström Dennis | |
2020-12-08 | 09:00 - 12:30 | F365 | Databearbetning | Biström Dennis | |
2020-12-10 | 14:00 - 15:45 | F365 | Databearbetning | Glögg i Altspace | Biström Dennis |