Course coordinator
Dr Bikesh Raj Upreti
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Business Data Management is offered as a part of the UQ’s Master of Business Analytics program. The course focuses on teaching skillsets that are fundamental in designing and managing transactional and informational databases. The topics in the course range from data modelling, creating and manipulating database tables, advanced database querying databases to integrating data, designing analytical databases, and working with big data.ﾠ
The course is organised into four different modules:
- Conceptual and Relational Data Modelling
- Relational Query Languages (SQL)
- Data Warehouse and Dimensional modelling
- Data at Scale
The course is built in a learning platform rich with an engaging learning experience. Each module is a blend of self-directed learning materials, interactive assessments, hands-on experience using the tools popular across industries, case examples, social discussions, and interactive live sessions. The course educates students on organisational data and takes them through the journey of transforming organisation data into analytical insights. After completing this course, students are expected to develop competency to leverage data for analytical and reporting purposes.
Course contact
Course staff
Lecturer
Dr Bikesh Raj Upreti
Professor Gianluca Demartini
Dr Hassan Khosravi
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Product/ Design | ER diagrams and the relational Model | 20% |
9/08/2024 4:00 pm |
| Computer Code | SQL | 30% |
6/09/2024 4:00 pm |
| Essay/ Critique | Dimension model and Big data case scenario | 50% (Dimension model 40%, Big data 10%) |
25/10/2024 4:00 pm |
Assessment details
ER diagrams and the relational Model
- Mode
- Product/ Artefact/ Multimedia
- Category
- Product/ Design
- Weight
- 20%
- Due date
9/08/2024 4:00 pm
- Learning outcomes
- L01
Task description
This assignment has two sections. The first part focuses on conceptual modelling and tests students' ability to capture important aspects of multiple systems that need to be stored in a database. The second part focuses on the relational model and tests students' understanding of integrity constraints and mapping ER diagrams to relational schema.
AI STATEMENT: "Students are permitted to use any form of AI to any degree in completing this assignment. However, they must clearly document any use of AI in each instance. Specifically, students are required to include copies of their interactions with the AI tool as an appendix at the end of their submission. Failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assessment will be submitted within the course's Learn.UQ site.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
SQL
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 30%
- Due date
6/09/2024 4:00 pm
- Learning outcomes
- L02
Task description
The purpose of this assignment is to test students' ability to use and apply SQL concepts to complete tasks in a real-world scenario. Specifically, this assessment will examine their ability to use SQL Data Manipulation Language to return specific subsets of information that exist in a database.
For this assignment, students will be presented with the simplified schema of an event management application. The goal of the application is to track both the events attended by users and relationships between users and other users. The system is then able to use this data to effectively market recommended events to users based on the events their friends have attended. You will be required to write 10 SQL queries that answer higher-level questions about the data in this database. (Note: Your queries must compile using a MySQL DBMS). A sample database will be provided to help you test your queries.
AI STATEMENT: Students are permitted to use any form of AI to any degree in completing this assignment. However, they must clearly document any use of AI in each instance. Specifically, students are required to include copies of their interactions with the AI tool as an appendix at the end of their submission. Failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assessment will be submitted on the course's Learn.UQ site.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Dimension model and Big data case scenario
- Mode
- Written
- Category
- Essay/ Critique
- Weight
- 50% (Dimension model 40%, Big data 10%)
- Due date
25/10/2024 4:00 pm
- Learning outcomes
- L03, L04
Task description
This Assignment evaluate learning outcomes 3) Develop skills to generate insights from organisational data, and 4) Solve challenges and leverage opportunities in dealing with Big Data.
The assignment has two components:
Dimension model: You will be given a case scenario for which you are required to answer the analytical questions. You are required to create dimension model, implement it using Microsoft SSIS, ETL tool, and answer the analytical question.
Big data: You will be provided with Big data scenario, and you will answer the questions related to the scenario.
Use of AI and generative tools: The assignment aims at developing critical thinking and communication skills. Writing should reflect the student's learning, reasoning, and critical thinking in an articulate way that fits the profile of the business student. We ask students to use an academic style of referencing content from scholarly works. As assessment tasks evaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI), use of AI is strictly prohibited. Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assessment will be submitted via the course's Learn.UQ site.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 64 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Not Timetabled |
Topic 1.1 Relational Databases and ER diagrams Self-Directed Learning: This topic first introduces relational databases and database management systems. It then introduces conceptual modelling using ER diagrams. Learning outcomes: L01 |
Case-based learning |
Content review and case study analysis We will review the important aspects of the content covered in the first week and discuss a case study related to ER diagrams. Learning outcomes: L01 |
|
Week 2 |
Not Timetabled |
Topic 1.2. The Relational Model Self-Directed Learning: This topic introduces the relational model including its main concept and components, integrity constraints, and how ER diagrams can be mapped into relational models. Learning outcomes: L01 |
General contact hours |
Consultation This will be a consultation session where we will discuss questions related to module one and the first assignment. Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Module 1: Self-Study Week Self-Directed Learning: This is a self-study week for revising the module and completing the Assignment. Learning outcomes: L01 |
Case-based learning |
Content review and case study analysis We will review the important aspects of the content covered in the second week and discuss a case study related to ER diagrams. Learning outcomes: L01 |
|
Week 4 |
Not Timetabled |
Topic 2.1 Introduction to SQL Self-Directed Learning: This topic presents an introduction to relational query languages and SQL including the Data Definition Language (DDL) and basic retrieval queries based on the Data Manipulation Language (DML). Learning outcomes: L02 |
Case-based learning |
Content review and case study analysis We will review the important aspects of the content covered in the fourth week and discuss a case study related to SQL Data Definition Language Learning outcomes: L02 |
|
Week 5 |
Not Timetabled |
Topic 2.2 Basic SQL Queries Self-Directed Learning: This topic focuses on basic SQL queries that use aggregation functions, GROUP BY and HAVING clauses on one or multiple relations. Learning outcomes: L02 |
General contact hours |
Content review and live demos We will review the important aspects of the content covered in the fifth week and run live demos of running SQL queries. Learning outcomes: L02 |
|
Week 6 |
Not Timetabled |
Topic 2.3 Advanced SQL Queries Self-Directed Learning: This topic focuses on advanced SQL queries based on subqueries, division and views Learning outcomes: L02 |
Case-based learning |
Content review and case study analysis We will review the important aspects of the content covered in the sixth week and discuss a case study related to SQL Data Manipulation Language Learning outcomes: L02 |
|
Week 7 |
Not Timetabled |
Module 2: Self-Study Week Self-Directed Learning: This is a self-study week for revising the module and completing the Assignment. Learning outcomes: L02 |
General contact hours |
Consultation This will be a consultation session where we will discuss questions content related to module two and the second assignment. Learning outcomes: L02 |
|
Week 8 |
Not Timetabled |
Topic 3.1 Data normalization and Data Warehouse Self-Directed Learning: This topic focuses on concepts related to data redundancies, data anomalies, data normalization, need for data denormalization, introduction to Data Warehouse, Star Schema, and creation of dimension models. Learning outcomes: L03 |
General contact hours |
Content review and ETL tutorial discussion We will review important aspects of the content covered in the eight week and discuss the tutorial related to ETL implementation using Microsoft SSIS. Learning outcomes: L03 |
|
Week 9 |
Not Timetabled |
Topic 3.2 Data warehouse architecture and ETL Self-Directed Learning: This topics focus on various techniques for creating facts and dimension tables data warehouse architecture, Extract transformation load (ETL), and Microsoft SQL Server integration services (SSIS) tutorial. denormalization, introduction to Data Warehouse, Star Schema, and creation of dimension models. Learning outcomes: L03 |
Case-based learning |
Content review and case study analysis Self-Directed Learning: We will review the important aspects of the content covered in the ninth week and discuss a case study related to dimension modelling. Learning outcomes: L03 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Not Timetabled |
Module 3: Self-Study Week Self-Directed Learning: This is a self-study week for revising the module and completing the Assignment. Learning outcomes: L03 |
General contact hours |
Consultation This will be a consultation session where we will discuss questions related to module three and the part one of the third assignment. Learning outcomes: L03 |
|
Week 11 |
Not Timetabled |
Topic 4.1 Big Data Self-Directed Learning: This topic presents an introduction to Big Data including scenarios and discusses the CAP (consistency, availability, and partition) Theorem and its implications on the capabilities and limitations of big data systems. Learning outcomes: L04 |
Case-based learning |
Content review and scenario analysis We will review the important aspects of the content covered in the eleventh week and the case related to big data. Learning outcomes: L04 |
|
Week 12 |
Not Timetabled |
Topic 4.2 Data Volume, Streams, and Graphs: Self-Directed Learning: This topic presents system architectures to process big data including, for example, Map/Reduce and Apache Spark. Learning outcomes: L04 |
General contact hours |
Content review We will review the important aspects of the content covered in the twelfth week. Learning outcomes: L04 |
|
Week 13 |
Not Timetabled |
Module 4: Self-Study Week Self-Directed Learning: This is a self-study week for revising the module and completing the Assignment. Learning outcomes: L04 |
General contact hours |
Consultation This will be a consultation session where we will discuss questions related to module four and the second part of the third assignment. Learning outcomes: L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.