Course syllabus autumn 2019
Course syllabus autumn 2019
Title
Digital methods for business and social innovation
Swedish title
Digitala metoder för affärsutveckling och social innovation
Course code
ME255A
Credits
15 credits
Grading scale
UA / Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Syllabus approval date
2019-02-15
Syllabus valid from
2019-09-02
Entry requirements
DA370A, DA371A, DA372A, or DA354A, DA271A, DA336A, or equivalent.
30 HE credits Informatics, 30 HE credits Computer and Information Science, of which at least 7.5 HE credits in programming.
Level
Basic level
Main field
Computer Science
Progression level
G1F
Progression level in relation to degree requirements
This is an elective course.
Course objectives
The purpose of the course is to provide the student with an understanding of how digital methods support business development and social innovation in finding patterns and insights from a large amount of data. Based on concrete cases, the student will learn basic concepts of how to select and apply adequate digital methods for the data mining, pre-processing, classification, segmentation, grouping, modelling, visualization, evaluation and analyzation of digital data. The student will work with cases that focus on Social Innovation in order to find solutions and improvements to social problems and needs in society.
Course contents
The course is comprised of the following elements:
- Basic concepts and orientation regarding digital methods and applications, with a focus on data extraction and analysis.
- The study of common types of digital extraction tasks and technologies within business development.
- The connection between data-driven business development and social innovation.
- Orientation on research design with support from digital methods.
- Programming (Python, Java, R) and the use of data visualization applications for working with data extraction and analysis.
Learning outcomes
Knowledge and understanding
On completing the course, the student shall:
- demonstrate the ability to explain important concepts in the field of data science and data extraction;
- demonstrate the ability to describe important steps in digital methods for handling, processing and visualizing data; and
- demonstrate insight into the application of and the challenges and opportunities presented by data analysis in practical problem-solving situations related to business development and social innovation.
Competence and skills
On completing the course, the student shall:
- demonstrate the ability to apply digital methods to large amounts of data in order to identify and analyze relevant information based on a formulated problem, as well as to present the process and results obtained.
Judgment and approach
On completing the course, the student shall:
- demonstrate the ability to evaluate the choice of relevant digital methods for various problems related to business development and social innovation; and
- demonstrate the ability to assess how the quality, amount and type of data steers the choice of digital method.
Learning activities
Problem-based activities based on practical cases; supported by lectures, workshops. seminars and laboratory work. Practical cases should preferably be prepared in collaboration with social enterprises and described in an individual report to be presented orally.
Assessment
Laboratory work (mini-projects, exercises) 5 HE credits
Seminars, theoretical concepts, 2 HE credits
Seminars, practical applications, 2 HE credits
Final individual report, case study, 5 HE credits
Final presentation, case study, 1 HE credit
In order to achieve a passing grade for laboratory work and seminars, the student is required to participate actively in and achieve a passing grade in each individual course component
In order to achieve a passing grade for the entire course, the student is required to achieve a passing grade for all laboratory work and seminars and a minimum of an E grade for goal attainment in all of the course’s intended learning outcomes. Goal attainment for each outcome is assessed based on the individual report and oral presentation with the aid of an assessment matrix, which will be provided by the course coordinator.
Course literature and other study material
- Provost, F. & Fawcett, T. (2015). Data Science for Business: What You Need to Know About Data Mining and Data-analytic Thinking. (1st Edition) Sebastopol, Calif.: O'Reilly.
- Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age. http://doi.org/10.1111/rssa.12375
Additionally, a collection of scientific articles will be used.
Course evaluation
At the end of the course, all students will be offered the opportunity to submit written comments on the course. A compilation of these comments and any remarks from the course coordinator will be discussed with students/course representatives at a course evaluation/programme committee meeting. The compilation will be made available on the department network. (HF 1:14)
Interim rules
If a course has been discontinued or its content has altered significantly, students shall, for a period of one year after such changes have been implemented, be offered two opportunities for retakes based on the course syllabus that applied at the time of registration.