MØLBA3001 Data Engineering

    • Course code
      MØLBA3001
    • Number of credits
      7,5
    • Teaching semester
      2023 Autumn
    • Language of instruction
      English
    • Campus
      Lillehammer
    • Required prerequisite knowledge

      None

Course content

This course provides students with the knowledge, tools, and skills to capture, clean, transform, and load data for further use in the organization. The topics covered are: 

  • Data types, structures, and sources 
  • Data acquisition 
  • Storage and systematization of data in databases and data warehouses 
  • SQL 
  • Cloud solutions and programming interfaces 
  • Peculiarities of Big Data 
  • Role of data management in creating business value

Learning Outcome

Knowledge

Upon completion of the course, the candidate shall: 

  • Have advanced knowledge about data types and structures and their functionality (k1)  
  • Explain and exemplify the process of gathering data from various sources (k2) 
  • Have advanced knowledge of key concepts of data storage in databases and data warehouses (k3) 
  • Have advanced knowledge about relevant information technology such as cloud solutions, software as service (SaaS), and programming interfaces (k4) 
  • Have advanced knowledge of peculiarities of ”Big Data” with regards to gathering and storage (k5) 
  • Know about ethical and legal issues related to gather and store data (k6) 
  • Explain the role of data engineering in creating and maintaining business value (k7) 
Skills

Upon completion of the course, the candidate shall be able to: 

  • Access and collect local and web-based data (f1) 
  • Extract, transform, and merge data using SQL (f2) 
  • Create and manipulate data sets of various data types using algorithms (f3) 
  • Contribute to designing or improving data storage and management systems in a business (f4)  
General competence

Upon completion of the course, the candidate shall be able to: 

  • Plan the various stages of a data engineering project to make the data ready to consumers for analytics and decision-making (g1) 
  • Recommend computing tools and techniques for efficient implementation of such projects (g2) 
Teaching and working methods

The following teaching methods are used: 

  • Lectures 
  • Problem solving sessions 
  • Tutorial videos 
  • Case studies 
  • Quizzes 
Required coursework
  • Mandatory homework assignments must be handed in before each teaching module (a total of 4). Two will be individual, and two will be in groups. These will be combinations of practical and theoretical exercises covering key topics in the course.  
  • Three out of four homework assignments must be passed to be allowed to take the exam.  
  • Attendance on at least 50% of the courses lectured teaching.
Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Home exam
ECTS - A-F
Individual
48 Hour(s)
  • All
60 %
Written examination with invigilation
ECTS - A-F
Individual
4 Hour(s)
  • No support materials
40 %
Form of assessment
  • 48 hours take-home individual exam (counts 60% of the grade). The exam consists of practical assignments and a written report. 
  • Four-hour individual school exam under attendance (counts 40% of the grade). 

Graded A-F, where E is minimum for passing the exam. Both exams must be passed for the student to pass the course.

Reading list

Course name in Norwegian Bokmål: 
Data Engineering
Faculty
Inland School of Business and Social Sciences
Department
Department of Business Administration
Area of study
Økonomisk-administrativ utdanning
Programme of study
Master of Science in Business Administration - majoring in Business Analytics
Course level
Second degree level (500-HN)