Bachelor
2021/2022
Data Management
Type:
Compulsory course
Area of studies:
Business Informatics
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
3 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
2
Contact hours:
20
Course Syllabus
Abstract
Managers need to be properly informed in order to take appropriate decisions to keep up with business successfully. Data possessed by organizations are usually scattered among different systems, each one devised for a particular kind of business activity. Further, these systems may also be distributed geographically in different branches of the organization. Data warehousing is nowadays a technology widely used by organizations to improve their operations and better achieve their objectives. Data warehouse accumulates information from different sources into a single repository suited for analysis. Development of analytical databases requires special approach that involves architectures, tools, and techniques for bringing together data from heterogeneous information sources. In this course students will learn to create architected and virtual data marts, design data flow and build business intelligence reports.
Learning Objectives
- The course familiarizes students with basic principles of building a data warehouse and focuses on data mart design, loading data and creating reports on top of star schema data source. Practice includes ETL procedures development, building a virtual data mart in SAP HANA platform and creating business intelligence reports in Tableau.
- Provide students with necessary knowledge and practical skills in business database design and maintenance
- Learn to use SQL programming language to read and process the data stored in a relational database
Expected Learning Outcomes
- Build a logical data model on top of a conceptual model
- Create database objects with SQL - procedures, functions and views
- Describe a relational database schema
- Develop complex queries with use of window functions
- Explore dependencies of attributes and solve data redundancy problems
- Find a plan for retrieving data from a relational DB in terms of relational operations
- Identify data model types
- Identify entities and relationships based on business rules analysis and create conceptual and logical models
- Organize data in table form
- Prepare reports and dashboards to provide access for users to business-critical information
- Retrieve data from connected tables
- Track and process changes in table data with triggers
- Use calculations in SQL queries
- Use client software to manage and fill a relational database
- Use IDEF1X method to create a database
- Use query editor to write and execute queries
- Use spreadsheet tools to store and process data
- Write analytical queries with aggregation
- Know basic components of data warehouse
- Know difference between architected and virtual data marts
- Know the difference between OLTP and OLAP systems
- Learn to build business intelligence reports on top of modern data processing platforms (SAP HANA 2)
- Students will be able to organize data flow from sources to staging area and data marts
- Understand dimensional modelling principles
- Understand functions of ETL tools (Talend)
- Understand main approaches to building a data warehouse
Course Contents
- Introduction to data management. Database systems
- Introduction to data warehouse design
- Relational algebra. Retrieving data from multiple tables in DQL
- Data warehouse architecture
- Database design
- ETL subsystem of data warehouse
- Data manipulations in structured query language. Create, read, update and delete operations.
- Virtual data mart and business intelligence systems
- Advanced SQL. Analytical queries. Procedural SQL
- Reporting and data visualization
Assessment Elements
- Team project
- Test
- Exam
- Practice
- Quiz on lecture
- Test
- Assignments on lecturesSmall assignments in form of tests will be given to the students at the end of each lecture.
- In class practice
- Team projectThe team size is 2-3 students. The results should be published on the shared database server. Reports should be uploaded on a cloud disk service.
Interim Assessment
- 2020/2021 4th module0.25 * Team project + 0.2 * Exam + 0.2 * Test + 0.1 * Quiz on lecture + 0.25 * Practice
- 2021/2022 1st module0.15 * In class practice + 0.15 * Assignments on lectures + 0.3 * Test + 0.4 * Team project
Bibliography
Recommended Core Bibliography
- Garcia-Molina, H., Ullman, J. D., Dawson Books, & Widom, J. (2014). Database Systems: Pearson New International Edition : The Complete Book (Vol. Second edition). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1418178
- Hoffer, J. A., Ramesh, V., & Topi, H. (2016). Modern Database Management, Global Edition (Vol. Global edition, Twelfth edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419666
- Kimball, Ralph, and Margy Ross. The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, 2013.
- Paulraj Ponniah. (2010). Data Warehousing Fundamentals for IT Professionals: Vol. 2nd ed. Wiley.
Recommended Additional Bibliography
- Clark, D. (2017). Beginning Power BI : A Practical Guide to Self-Service Data Analytics with Excel 2016 and Power BI Desktop (Vol. Second edition). Camp Hill, Pennsylvania: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1478775
- Foster, E. C., & Godbole, S. (2016). Database Systems : A Pragmatic Approach (Vol. Second edition). [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174505
- Inmon, W. H., Linst, D., & Levins, M. (2019). Data Architecture: A Primer for the Data Scientist : A Primer for the Data Scientist (Vol. Second Edition). London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1951596