Syllabus for Data Analytics
A revised version of the syllabus is available.
- 7.5 credits
- Course code: 2IS239
- Education cycle: First cycle
Main field(s) of study and in-depth level:
Information Systems G2F
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (G), Pass with distinction (VG)
- Established: 2018-04-26
- Established by:
- Revised: 2018-10-25
- Revised by: The Department Board
- Applies from: Spring 2019
60 credits in information systems including 60 credits in databases
- Responsible department: Department of Informatics and Media
Regarding knowledge and understanding the student is expected to be able to on completion of the course:
- describe key concepts, applications, and algorithms within data analytics,
- explain areas where data analytics can be applied in an organisational context.
Regarding competence and skills the student is expected to be able to on completion of the course:
- use tools to analyse, find patterns in, and visualize large amounts of data,
- compile and convert data from different data sources and data formats,
- through programming create solutions for basic analysis and visualization of data.
Regarding judgement and approach the student is expected to be able to on completion of the course:
- identify and discuss ethical issues in relation to data analytics,
- critically discussing methodological challenges associated with data analytics.
The course provides both theoretical and practical knowledge and skills regarding storage, processing, analysis, and visualization of data. This includes compiling and investigating data to find patterns that can be useful in the organisation, for example, in the form of improved decision-making for the organisation's management. The course addresses concepts related to data analytics, such as big data, data lakes, machine learning, and visualization. The practical aspects of the course consist partly of the application of tools for analysis and visualization, and partly by laboratory exercises where the students develop their solutions in the field. In the course, students also learn how to compile data from different sources to enable data analysis. Data analytics can create great benefits for organisations, but also involve risks to individuals and society. The course, therefore, highlights ethical issues regarding data analytics.
Teaching is given as lectures, seminars, supervision and laboratory work. Mandatory attendance may occur.
The course is examined through assignments, seminars, laboratory work and participation in compulsory parts.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.
- Latest syllabus (applies from Spring 2021)
- Previous syllabus (applies from Spring 2019, version 2)
- Previous syllabus (applies from Spring 2019, version 1)
A revised version of the reading list is available.
Applies from: Spring 2019
Some titles may be available electronically through the University library.
Rogel-salazar, Jesus (university Of Hertfordshire
Data science and analytics with python
Taylor & Francis Inc, 2017
Reading list revisions
- Latest reading list (applies from Autumn 2020)
- Previous reading list (applies from Spring 2019)