Study Guide

Objective

After completing the course the student can:
- understand different types of databases and evaluate their feasibility for different purposes.
- plan and implement a database based on requirements and search and modify data in the database
- use at least one well-known database management system
- describe database management tasks

Content

- Different types of databases
- Definition, planning and implementation of databases
- SQL basics
- Database administration with DBMS
- Management principles for databases

Materials

Lecture slides and examples by the teacher
Lot of internet material available
Supporting books about relational databases and SQL are available in Internet.
MongoDB has good tutorials and documentation as well.

Teaching methods

The course consists of
1) lecture and home exercises (small queries and design tasks)
2) personal practical work (creating your own database) and
3) exam (testing your acquired skills).

Lecture exercises are divided into weekly topics. Each week introduces a new topic that builds on top of previous weeks. Each lecture begins with an introduction to the topic of the week, which includes practical examples and learning material. Exercises are done individually or in small groups with the help of the teacher .

NOTE! Lecture exercises can be returned only by participating in the lecture session!

Exam schedules

The exam is performed in ViLLE system www.ville.utu.fi which supports SQLite.
Exam and Re-exams, i.e. 2nd and 3rd exams, are e-exams in the e-exam room premises (EduCity, Library) where Internet use is not allowed. E-exams are open 6 months after the course has ended.

International connections

- Learning by doing and trial & error with lecture exercises,
- Introductory lectures and examples provided by the teacher.
- Collaborating with other students in the lectures.

Completion alternatives

Participation in the lecture is not compulsory, but exercises can be returned only during the lecture.

Online course is available for those who can't participate on lectures. This self-study option has slightly different emphasis of topics and grading. These will be introduced in the beginning of the course in the first lecture. Students can choose their preferred method after the first lecture.

Student workload

Introduction lecture 2h
Participating weekly in lectures (exercises): a' 3 hours * 13 = 40h
Home exercises 10h
Individual practical work 60h
Exam + preparing 20h

Student workload is about 5-8 h / week if you are new to relational databases.

Content scheduling

In this course, students learn to use and design relational databases as well as understand differences to NoSQL/document databases. First, students familiarize with database thinking and the principles of data management from a quality perspective. Key topics include data modeling using ER diagrams, relational schema representations and normalization technique for validating the quality of the database design. Second, students apply structured query language (SQL) to create a database (SQL DDL), and to manipulate and search data in the database (SQL DML). Last, students learn differences between SQL and NoSQL databases through desinging and using MongoDB document database. The course consists of lectures, exercises, a practical work and final exam.

Topics (and hours used in teaching sessions) in the order of appearance:
- Relational DBMS and DB use 6h
- Relational database design 9h
- Basics of SQL 18h
- Introduction to document database MongoDB 6h

Further information

All returns and communications take place through the It's Learning platform (except for the online course).

There are no pre-requisites for course performance in this course, and this course does not require previously acquired skills. It is necessary to have your own computer and know how to use it.

We use the relational database and its management environment for practical training (MySQL, MariaDB, SQLite or similar used in UwAmp, XAMPP or WAMP or similar) and must be installed on the student's personal computer. The necessary applications are installed in a lecture together.

In addition to relational databases, students learn about MongoDB cloud services, Mongo Shell, and practice designing and using a document-based database.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded from 0-5. The grade is based on collected points during the course.

Each returned exercise is 1 point unless mentioned otherwise. The exam is compulsory part and must be passed with 40% of total points of the exam, in order to pass the course.

Division of points:
Lecture and home exercises 70 p points in total
Practical work 70 points
Exam 60 p points
Total 200 points

Course grading:
Points Grade
0-99 NOT PASSED
100-119 1
120-139 2
140-159 3
160-179 4
180-200 5

Assessment criteria, fail (0)

Less than 50% of total points collected or the exam is failed (less than 40% of total points of the exam). Check the points-to-grade table

Assessment criteria, satisfactory (1-2)

- Is able to implement relational database management software (DBMS) and know the tasks related to database maintenance
- Is able to design a relational database using conceptual model technique (ER or similar notation)
- Can implement a relational database with SQL statements
- Can retrieve, add and edit data in a relational database with simple SQL statements
- Knows different types of databases and their uses

Less than 70% of total points collected.

Assessment criteria, good (3-4)

In addition
- Can interpret the concept model and implement a relational database based on it
- Understands the meaning and use of keys and reference integrity in relational databases
- Is able to use SQL statements for data retrieval in various ways, such as combining data from different tables
- Understands the principle and purpose of normalization
- Can introduce non-relational databases and evaluate their suitability for different purposes (MongoDB)

70-90% of total points collected.

Assessment criteria, excellent (5)

In addition
- Is able to independently develop a high-quality concept model based on the user requirements
- Can use normalization to improve the quality of a relational database
- Can use SQL statements for information retrieval in various ways, such as sub-groupings and sub-queries
- Can do basic queries and design a simple NoSQL database (MongoDB)

More than 90% of total points collected.