BDA211 - Data Warehousing

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BN110 Information Systems Fundamentals

Co-requisite: BN204 Database Technologies

Aims & Objectives

This is a core unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of data analytics in Information Technology. For further course information refer to: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will learn how to design, develop and evaluate data warehousing solutions. They will gain knowledge of data storage, data wrangling, and data governance in data warehousing, as well as practical skills in data acquisition, cleansing, and decision modelling in data analytics.

This unit will cover the following topics:

  • Data warehouse architecture, concepts, schemas and components
  • Data storage, wrangling, and processing models
  • Data analysis with online analytical processing (OLAP)
  • Data warehouse planning and implementations
  • Data mining techniques: clustering, decision tree, and association rules
  • Business security and data integrity 
  • Data warehousing in big data applications

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Understand the concept and use of data warehousing in data analytic applications;
b. Appraise suitable data storage, wrangling and processing models in alignment with business objectives and ethical standards;
c. Design appropriate data warehousing architecture to the specification;
d. Deploy selected data warehousing models in data analytic applications;
e. Evaluate data warehousing models in relation to the ethics standards and specifications.
 

Weekly Topics

This unit will cover the content below:

Week Topics
1 Data warehouse basics
2 Data warehouse architecture – conceptual, logical and physical
3 Data extraction, transformation, and loading (ETL); and data cleansing
4 Data wrangling – data dimensionality control
5 Data ethics, quality assurance and governance
6 Online analytical processing (OLAP) Advanced OLAPs: ROLAP and MOLAP
7 Data mining techniques 1 - clustering and decision tree
8 Data mining techniques 2 - association mining rules
9 Data mining applications – big data analysis
10 Data warehouse ethics, maintenance and security
11 Data warehouse Implementation
12 Review and future applications of data warehousing

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B		 Week 3
Week 8		 5%
10%		   a-c
Assignment 2 Week 11 35%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   40% a-e
TOTALS   60% 40%  

Task Type: Type A: unsupervised, Type B: supervised.

Class Participation and Contribution
This unit has class participation and student contribution as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials


Textbook:

  • Parteek Bhatia, Data Mining and Data Warehousing Principles and Practical Techniques, Cambridge University Press, 2019.

References:

  • Christian Coté, Michelle Kamrat Gutzait, Giuseppe Ciaburroo, Hands-On Data Warehousing with Azure Data Factory: ETL techniques to load and transform data from various sources, both on-premises and on cloud, Packt Publishing, 2018
  • Seema Acharya, Data Analytics using R, McGraw-Hill Education, 2018
  • N. Dasgupta, Practical Big Data Analytics, Packt Publishing, 2018

Adopted Reference Style: IEEE
 

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.