The course covers fundamental techniques for developing data management and data analytics applications.
The course covers fundamental techniques for developing data management and data analytics applications. The main part of the course deals with traditional relational database processing, including the theory and practice of modelling and querying a database. In the latter part of the course, the focus is on new developments for both traditional database applications and for modern data analytics applications.
This course assumes basic ability to use a computer. We also assume that a student has taken an introductory programming course (otherwise basic programming skills in Java are expected) and a discrete math course such as Discrete Mathematic for SD (or has knowledge of basic discrete math: logic and set theory).
Intended learning outcomes
After the course, the student should be able to:
- Write SQL queries, involving multiple relations, compound conditions, grouping, aggregation, and subqueries.
- Use SQL programming constructs in a secure manner.
- Suggest a database design in the E-R model and convert to a relational database schema in a suitable normal form.
- Analyze/predict/improve query processing efficiency of the designed database using indices.
- Reflect upon the evolution of the hardware and storage hierarchy and its impact on data management system design.
- Discuss the pros and cons of different classes of data systems for modern analytics and data science applications.
Lectures cover the most important aspects of the curriculum, and also provide tools and methods for describing, creating and using databases. Weekly exercise sessions consist of assignments designed to further understand and apply the topics discussed in lectures. The course has 4 optional assignments.
The course has no mandatory assignments.
The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data - Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens
Cambridge University Press; 1 edition (August 30, 2018)
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 45%
- Lectures: 12%
- Exercises: 14%
- Assignments: 19%
- Exam with preparation: 10%
Ordinary examExam type:
C: Submission of written work, External (7-point scale)
C22: Submission of written work – Take home
The exam is 4 hours followed by random fraud control.
Aids allowed for the exam: Written and printed books and notes. E-books on laptops, iPads, and other e-book readers are permitted.
Students must use their own computer with the PostgreSQL database system installed, as well as the Zoom software system.
Time and dateExam preparation Tue, 17 Mar 2020, 14:00 - 15:00
Exam preparation Tue, 17 Mar 2020, 19:00 - 20:00
Ordinary Exam - submission,Ordinary Exam - hand out Wed, 20 May 2020, 10:00 - 14:00
Reexam - on premises Thu, 13 Aug 2020, 15:00 - 19:00