Syllabus

Data Collection and Data Quality

Code
AMI23K
Points
7.5 Credits
Level
Second Cycle
School
School of Information and Engineering
Subject field
Microdata Analysis (XYZ)
Group of Subjects
Other Interdisciplinary Studies
Disciplinary Domain
Natural Science, 100%
This course can be included in the following main field(s) of study
Microdata Analysis1
Progression indicator within (each) main field of study
1A1N
Approved
Approved, 17 October 2019.
This syllabus is valid from 16 December 2019.
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Learning Outcomes

Upon completion of the course students will be able to
  • describe and show understanding of the importance of various types of data and various forms of data collection,
  • use and evaluate various types of data and various types of data collection,
  • identify important factors that contribute to data quality and data collection quality,
  • develop and evaluate a new data collection system,
  • ensure that data collection can be replicated in another context,
  • critically analyse the importance of meta data,
  • describe and show understanding of the significance of laws that regulate data storage.

Course Content

The course considers various types of data as well as various forms of data collection: for example, the collection of data over time, data collected by means of different types of sensors, selection analysis, and experimentation.
Throughout the course, there will be analysis of the laws regulating data collection, strategies that can be used for better data collection and data quality, and the connection between the collection and storage of data.

Assessment

An individual project, preparation and reporting, 3 Credits, laboratory reports, 2 Credits and individual assignments, 2.5 Credits. For assessment, students must actively participate in at least two thirds of the timetabled labs.

Forms of Study

Lectures, labs, project work, and seminars.

Grades

The Swedish grades U–VG.

Laboratory reports, U-G.
The final grade for the course is based on an overall assessment by the examiner.

Prerequisites

  • Bachelor’s degree in Statistics, Economics, Business Administration, Computer Science, Information Science or Informatics comprising at least 180 credits and English 6

Other Information

Replaces ST3012.