Course syllabus

Course-PM

DIT032 / DAT335 Data Management HT19 (7,5hp)

Last Revised January 03rd, 2019

Department of Computer Science and Engineering, Software Engineering division

 

This is an English-speaking course. Questions, either personally or via email, can only be answered if asked in English.

Course purpose

The purpose of this course is to give students a solid theoretical and practical background in the fundamentals of database design and usage. A focus is put on data management from a software developer's point of view. Further, in addition to traditional data modeling topics, relational theory, and SQL, the course teaches modern database concepts, such as soft-state NoSQL databases, Big Data, and Map/Reduce. At the end of the course, students are expected to be able to design, implement, and use a range of different database technologies in their software projects.

Schedule

Classes and supervisions are typically held every week during Study Period 3, but please check the Schedule if there actually is a class/supervision at any given day. We reserve the right to cancel individual classes and supervisions, for instance when there is no ongoing assignment to discuss. The schedule has a detailed list of in-class activities, and it will also say whether a class is scheduled (and what it is about).

Contact details

Examiner / Main Teacher
Philipp Leitner (philipp.leitner@chalmers.se)

Teaching Assistant Coordinator / Responsible Person for Assignments
Joel Scheuner (scheuner@chalmers.se)

Teaching Assistants

David

Lindgren

guslindaaq@student.gu.se

Chi Hong (Nigel)

Chao

guschaoch@student.gu.se

Ilja

Pavlov

guspavloil@student.gu.se

Ranim

Khojah

guskhojra@student.gu.se

Course literature

We are primarily following the book Fundamentals of Database Systems (7th Edition). More details are available in the Literature section.

Course design

The course is based on three didactic pillars:

  1. Traditional up-front teaching through lectures. Participation in lectures is optional but strongly suggested.
  2. Lectures are intended to be enhanced through self-learning, primarily by weekly studying the relevant chapters in the book. In addition to reading the chapters, students are expected to solve (parts of) the book exercises on their own. These voluntary exercises are not graded, but solutions can be discussed either online in the forum or in supervisions.
  3. In addition to voluntary exercises, there are three graded assignment sheets. Assignments need to be solved in teams of two students, and they are graded. Assignments need to be solved outside of class, but they can be discussed during supervision.

Discussions in the Canvas Discussions section are a big part of the learning experience in this course, so any technical questions should be directed to the forum first.

Changes made since the last occasion

  • Small changes to the teaching and learning activities
  • Border for VG / 5 has been lowered to 85% (was 90% in the previous iteration)

Examination form

The course is examined using a combination of mandatory assignments and by a final written exam. The date for the (first) final examination is 18. Mar 2019. A student who has failed the examination has the right for a re-examination. The next two re-examination dates are 11. Jun 2019 and 21. Aug 2019. 

The students need to submit three assignments during the course. Assignments are solved in groups of two students, and each group submits a single assignment which "counts" for both students. Groups are registered at the beginning of the course and remain the same for the entire course. Exceptions can only be made if a student cannot continue to participate in the assignments, for instance, due to severe illness. An assignment counts as passed when the group achieves at least 70% of the total points for the assignment. All assignments together are worth 3.0 HEC. All assignments need to be passed in order to pass the entire course. Students are allowed to re-submit failed assignments until the end of the course (up to in total 3 submissions, including the original assignment submission). However, this requires a short oral exam and a detailed changelog in addition to the submission of an improved assignment sheet.

There is a hard deadline for each assignment, by which it needs to be submitted electronically via Canvas. Late submissions are not accepted and count as failed assignments. However, failed assignments can be re-submitted during the course. In this case, a short oral exam is required in addition to submitting the assignment sheet.

Students need to individually pass a written exam at the end of the course; the written exam corresponds to 4.5 HEC. The written exam is passed when the student achieves at least 50% of the total points for the final exam. For GU, a pass with honor (VG) is given for the entire course if the student passes all three assignments and reaches at least 85% of the total points in the written exam. For Chalmers, 85% of the total points are required for a 5, 70% for a 4, and 50% for a 3.

A student will get a Fail (U) in case of plagiarism/cheating during the written exam or during the assignments. Assignments count as plagiarized if students submit (parts of) another group's assignment solution. Further, we will count it as cheating if groups enable others to plagiarize by uploading significant parts of their solution to a public web space (e.g., GitHub).

Learning objectives and syllabus

After completing the course the student will be able to:

  • explain the differences between data, information, and knowledge

  • explain basic concepts of database theory, such as relational data model, non-relational data model, entity-

    relationship model, relational database design, relational algebra, and the database language SQL

  • construct an algorithm for filtering data based on a predefined criterion

  • manage the process of collecting and representing data in a database

  • build a data model (entity-relationship model)

  • create database tables, and formulate database queries in SQL

  • experiment with big data technologies

  • assess the quality of data and correctness of data models

  • evaluate the applicability of data management techniques for a given purpose

Link to Syllabus

 

Course Evaluation

GU-students: For course evaluation questionnaire please click here! (accessible starting the 24th of March) 

CTH-students: a course evaluation questionnaire will be sent out seperatly. 

Course summary:

Date Details Due