Data Systems and Analysis (5cr)

Objectives (course unit)

The student learns basic concepts, ideas and principles concerning database systems including big data systems. The student is able to install, configure and run a database system and to design and implement data content to it and handle it with SQL language. General big data system concepts are handled; the aim is that the student can install, configure and run a big data system and handle it’s data. Some of the most popular big data technologies are learned.

Content (course unit)

Course content is:
- General ideas of a database system,
- Data modelling and design techniques,
- Relational model, data handling with SQL language,
- Database programming,
- NoSQL databases
- General ideas of a big data system
- CRISP DM

Assessment criteria, satisfactory (1-2) (course unit)

Student knows database system concepts and ideas and recognises them in real running systems. The student is able to do modest maintenance works for existing database systems and participate partly in the design of new database systems.

Assessment criteria, good (3-4) (course unit)

Student understands database system concepts and ideas and can justify their existence in real running systems. The student is able to maintain existing database system structures and design of new database system parts.

Assessment criteria, excellent (5) (course unit)

Student knows and understands in depth database system concepts and ideas and is familiar with their existence in real running systems. The student is able to create new database system structures and make new designs in all areas of the database systems.

Location and time

Fall 2019, weekday and time, place is in "lukkarikone". If there are changes I will inform with tabula messaging.

Exam schedules

-

Assessment methods and criteria

Bases on exercise / lab works activity. Grading:

Activity below 20 % => grade = 0
Activity between 20 % and 35 % => grade = 1
Activity between 35 % and 50 % => grade = 2
Activity between 50 % and 65 % => grade = 3
Activity between 65 % and 80 % => grade = 4
Activity above 80 % => grade = 5

Assessment scale

0-5

Teaching methods

Theory lectures, exercises and lab works.

Learning materials

Elmasri & Navathe: Fundamentals of Database Systems. 7h edition. 2017 Pearson Global Edition.

Kroenke & Auer: Database Processing Fundamentals, Design, and Implementation. 14th edition. 2016 Pearson Global Edition.

Student workload

Lecturing: 3 hours per week approximately 13 times.

You should reserve approximately 135 hours time to all works in this course (5 points times 27 hours per point).

Content scheduling

1. General database related topics
2. ER-modeling
3. SQL
4. Programming with databases
5. NoSQL databases
6. Big data systems

Completion alternatives

-

Practical training and working life cooperation

-

International connections

-

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Doesn't know the basic concepts handled in this course and cannot apply them.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

A satisfactory understanding of the concepts and things taught in the course. Can apply them with help.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

A good understanding of the concepts and things taught in the course. Can apply them independently.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

An excellent understanding of the concepts and things taught in the course. Can apply them independently. Can discover new things in the area and learn them individually. Can apply the skills in new ways combining different areas of ICT systems.