Introduction to Database Systems, SWU
AbstractThe course covers fundamental techniques for developing data management and data analytics applications.
DescriptionThe 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 (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. The weekly exercise sessions are intended to support students in their learning, either via specific assignments designed to help students understand and apply the topics discussed in lectures, or via students focusing on topics of their own choice. There are 4 homework assignments, that receive general and/or individual feedback.
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
4 hour online take home exam.
Written and printed books and notes
E-books and/or other electronic devices
- E-books on laptops, iPads, and other e-book readers are permitted.
- Any course material can be accessed online or on your machine.
- Your own notes can be accessed in any form.
Specific software and/or programmes
- Students must use their own computer with the PostgreSQL database system and their database client of choice installed.
- Use of (online or local) drawing programs for ER diagram creation is permitted.
- Use of online documentation for PostgreSQL (https://www.postgresql.org/docs/) and SQL (e.g., https://www.postgresqltutorial.com and https://www.w3schools.com/sql/) is permitted.
Communication with others is strictly forbidden. This includes posting anything to the internet to sites such as stackexhange. Searching for information on the internet (“googling”) is forbidden.
Random fraud control with Zoom will be conducted right after the submission.
Student Affairs and Programmes will randomly select 20 % of students who will have to show up in Zoom to check authorship of submitted solutions. The selection of students for fraud control will be published in LearnIT right after the exam together with a link to the Zoom meeting.