CQUniversity Unit Profile
COIT12209 Data Science
Data Science
All details in this unit profile for COIT12209 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

Overview

This unit focuses on the foundational concepts of data science. Digital data is growing at a very fast rate with data being the underlying driver of the knowledge economy. This unit will prepare you with foundational knowledge and practical skills about data collection, representation, storage, retrieval, management, analysis, and visualisation through the exploration of data-related challenges. You will also learn the impact of big data and business analytics on business performance to cater for the development of useful information and knowledge in an attempt to achieve data-driven decision making.  

Details

Career Level: Undergraduate
Unit Level: Level 2
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125

Pre-requisites or Co-requisites

Prerequisite: COIT11226 Systems Analysis Co-requisite: COIT11237 Database Design & Implementation

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Offerings For Term 3 - 2024

Online

Attendance Requirements

All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Class and Assessment Overview

Recommended Student Time Commitment

Each 6-credit Undergraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Class Timetable

Bundaberg, Cairns, Emerald, Gladstone, Mackay, Rockhampton, Townsville
Adelaide, Brisbane, Melbourne, Perth, Sydney

Assessment Overview

1. Practical Assessment
Weighting: 40%
2. Written Assessment
Weighting: 40%
3. Presentation
Weighting: 20%

Assessment Grading

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of 'pass' in order to pass the unit. If any 'pass/fail' tasks are shown in the table above they must also be completed successfully ('pass' grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the 'assessment task' section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University's Grades and Results Policy for more details of interim results and final grades.

Previous Student Feedback

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Feedback from Unit Evaluation

Feedback

Practice materials on the R language were insufficient.

Recommendation

Provide additional materials on R langauge.

Feedback from Unit Evaluation

Feedback

Big data lecture materials (i.e., Hadoop) were not updated.

Recommendation

Review the lecture slides on big data, and revamp the lecture materials.

Unit Learning Outcomes
On successful completion of this unit, you will be able to:
  1. Discuss and demonstrate data science foundational concepts
  2. Investigate and evaluate applications for data storage, management, retrieval, and analysis and visualisation
  3. Apply knowledge to process data for data driven decision making
  4. Analyse and generate solutions to solve data-related challenges
  5. Demonstrate the knowledge required in using data science skills to solve business problems.

Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is in use in over 100 countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles.

ACS members can use the tool MySFIA to build a skills profile at https://www.acs.org.au/professionalrecognition/mysfia-b2c.html

This unit contributes to the following workplace skills as defined by SFIA. The SFIA code is included:

Data Management (DATM)

Business Analysis (BUAN)

Data Analysis (DTAN)

IT Operation (ITOP)

Alignment of Learning Outcomes, Assessment and Graduate Attributes
N/A Level
Introductory Level
Intermediate Level
Graduate Level
Professional Level
Advanced Level

Alignment of Assessment Tasks to Learning Outcomes

Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Practical Assessment - 40%
2 - Written Assessment - 40%
3 - Presentation - 20%

Alignment of Graduate Attributes to Learning Outcomes

Graduate Attributes Learning Outcomes
1 2 3 4 5
1 - Communication
2 - Problem Solving
3 - Critical Thinking
4 - Information Literacy
5 - Team Work
6 - Information Technology Competence
7 - Cross Cultural Competence
8 - Ethical practice
9 - Social Innovation
10 - Aboriginal and Torres Strait Islander Cultures

Alignment of Assessment Tasks to Graduate Attributes

Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8 9 10
1 - Practical Assessment - 40%
2 - Written Assessment - 40%
3 - Presentation - 20%
Textbooks and Resources

Textbooks

Supplementary

Amazon Web Services in Action

Third Edition (March 2023)
Authors: Andreas Wittig and Michael Wittig
Manning publishers
ISBN: 9781633439160

Please source the third edition of this book which is latest and was printed in March 2023. Versions available in CQU library are older versions.  Below is the link for this book. 

https://www.manning.com/books/amazon-web-services-in-action-third-edition

Please source the third edition of this book which is latest and was printed in March 2023. Versions available in CQU library are older versions.  Below is the link for this book. 

https://www.manning.com/books/amazon-web-services-in-action-third-edition

Supplementary

Data Engineering with AWS

Second Edition (2023)
Authors: Gareth Eagar
Packt Publishing
ISBN: 9781804614426
Supplementary

Data Science on AWS

(2021)
Authors: Chris Fregly, Antje Barth
O'Reilly Media, Inc.
ISBN: 9781492079392

Below is the link for this below.

https://learning.oreilly.com/library/view/data-science-on/9781492079385/

Below is the link for this below.

https://learning.oreilly.com/library/view/data-science-on/9781492079385/

Supplementary

Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

(2016)
Authors: Mendelevitch, O, Stella, C & Eadline, D
Addison-Wesley Professional
ISBN: 9780134024141

IT Resources

You will need access to the following IT resources:
  • CQUniversity Student Email
  • Internet
  • Unit Website (Moodle)
  • Zoom capacity (webcam and microphone) will be required for online students
  • Python 3.10 (or higher)
  • Anaconda 2023 or latest
  • Jupyter Notebook 7 or latest
  • Spyder integrated development environment (IDE) latest version
  • RStudio (IDE) and R
Referencing Style

All submissions for this unit must use the referencing style: Harvard (author-date)

For further information, see the Assessment Tasks.

Teaching Contacts
Ambi Jayal Unit Coordinator
a.jayal@cqu.edu.au
Schedule
Week 1 Begin Date: 04 Nov 2024

Module/Topic

Introduction to Data Science: What is data science; data domination; innovation from internet giants; data science history; data science in modern enterprises; soft skills of a data scientist; data science project life cycle; types of data; big data; how is big data different.

Chapter

Events and Submissions/Topic

Week 2 Begin Date: 11 Nov 2024

Module/Topic

Identifying Data Problems: From business problems to data mining tasks; data mining tasks; data collection; business use cases; sampling; data mining process.

Chapter

Events and Submissions/Topic

Week 3 Begin Date: 18 Nov 2024

Module/Topic

Hadoop and Data Science: Storage requirements; what is Hadoop; Hadoop's evolution; Hadoop tools for data science;

Chapter

Events and Submissions/Topic

Week 4 Begin Date: 25 Nov 2024

Module/Topic

Data Presentation: Understand different ways of summarizing data; choose the right table/ graph for the right data and audience; self explanatory graphics; attractive graphs and tables.

Chapter

Events and Submissions/Topic

Week 5 Begin Date: 02 Dec 2024

Module/Topic

Data Analytics: Why analytics; different types of analytics; delivery methods for the operational users; holistic approach to expand enterprise analytics; value of integration and data quality to analytics.

Chapter

Events and Submissions/Topic

Week 6 Begin Date: 09 Dec 2024

Module/Topic

Exploratory Analysis: trend analysis; Box plot; pairs plot; time series decomposition; geographical analysis.

Chapter

Events and Submissions/Topic

Assessment 1 - Practical Assessment (40% weighting) - Due week 6, Friday, 11:45 pm AEDT


Practical Assessment Due: Week 6 Friday (13 Dec 2024) 11:45 pm AEST
Week 7 Begin Date: 16 Dec 2024

Module/Topic

Data Discovery and Data Mining: Data driven decisions; enabling data driven innovations; knowledge discovery process; data cleaning; data integration; data selection; data transformation; knowledge based systems; data mining and its goals;data mining operation and process.

Chapter

Events and Submissions/Topic

Vacation Week Begin Date: 23 Dec 2024

Module/Topic

Chapter

Events and Submissions/Topic

Vacation Week Begin Date: 30 Dec 2024

Module/Topic

Chapter

Events and Submissions/Topic

Week 8 Begin Date: 06 Jan 2025

Module/Topic

Analytic Algorithms: clustering analysis; regression analysis; classifier analysis; association analysis; cohort analysis; graph analysis; traverse pattern analysis.

 

Chapter

Events and Submissions/Topic

Week 9 Begin Date: 13 Jan 2025

Module/Topic

Data Integration: Analytic data integration; challenges in data integration; technologies in data integration; data mapping; data staging; data extraction; data transformation; data loading; need for integration; data integration approaches.

Chapter

Events and Submissions/Topic

Week 10 Begin Date: 20 Jan 2025

Module/Topic

Data Security and Privacy: protection of personal data; data collection and significant risks; challenges of big data for data protection; confidentiality; integrity; availability; middleware security concerns; built in database protection; privacy issues; data security and storage; identification and authentication.

Chapter

Events and Submissions/Topic

Week 11 Begin Date: 27 Jan 2025

Module/Topic

System Design: An overview of ML systems in the real world

Chapter

Events and Submissions/Topic

Written Assessment (40% weighting) - Due week 11, Friday, 11:45 pm AEDT


Written Assessment Due: Week 11 Friday (31 Jan 2025) 11:45 pm AEST
Week 12 Begin Date: 03 Feb 2025

Module/Topic

Cloud Computing for Data Processing

Chapter

Events and Submissions/Topic

Assessment 3 - Presentation (20% weighting) - Due week 12, Friday, 11:45 pm AEDT


Presentation Due: Week 12 Friday (7 Feb 2025) 11:45 pm AEST
Exam Week Begin Date: 10 Feb 2025

Module/Topic

Chapter

Events and Submissions/Topic

Term Specific Information

Unit Coordinator: Dr Ambi Jayal
Email: a.jayal@cqu.edu.au

Assessment Tasks

1 Practical Assessment

Assessment Title
Practical Assessment

Task Description

This assessment is designed to reinforce the contents taught in Week 1 to Week 5. This assessment relates to Learning Outcomes 1 and 2. This assessment is an individual assessment and should be submitted in Week 6. You will submit work on the data processing exercise. This will provide you with an opportunity to learn data storage and processing. Each week you will be presented with a data-related challenge, and you will use computer tools to manipulate data to solve that challenge. This task will help to build your knowledge of data formats, retrieval, and analysis techniques. This assessment contributes to 40% of the total marks.

 


Assessment Due Date

Week 6 Friday (13 Dec 2024) 11:45 pm AEST


Return Date to Students

Within two weeks of submission


Weighting
40%

Assessment Criteria

The assessment will be marked based on the following criteria:

    Quality of source code
    Submitted screen shot of outputs
    Analysis presented on the generated output
    Well-structured and coherent report

More details will be available on the Moodle site.


Referencing Style

Submission
Online

Submission Instructions
All files must be submitted to Moodle for marking by the due date.

Learning Outcomes Assessed
  • Discuss and demonstrate data science foundational concepts
  • Investigate and evaluate applications for data storage, management, retrieval, and analysis and visualisation


Graduate Attributes
  • Communication
  • Critical Thinking
  • Information Literacy
  • Information Technology Competence

2 Written Assessment

Assessment Title
Written Assessment

Task Description

This assessment is based on a case study to be provided to you in teaching Week 6. You are required to write a report of 2000 words. This is an individual assessment and contributes to Learning Outcomes 2, 3, 4 and 5. This report will follow a standard business report format. You will investigate and advise an organisation, whose details are given in the case study, on data storage, retrieval, and analysis mechanisms. You will also develop an analytic dashboard for the organisation. This assessment contributes to 40% of the total marks.


Assessment Due Date

Week 11 Friday (31 Jan 2025) 11:45 pm AEST


Return Date to Students

Feedback and marks for this assessment will be released after the certification date as this unit does not have an exam.


Weighting
40%

Assessment Criteria

The assessment will be marked based on the following criteria:

Report formatting (font, header and footer, table of contents, numbering, referencing)
Professional communication (correct spelling, grammar, formal business language used)
Executive summary
Report introduction
Data collection and storage
Data in action
Model design and implementation
Conclusion and recommendations

More details will be available on the Moodle site.


Referencing Style

Submission
Online

Submission Instructions
The assignment must be submitted to Moodle for marking by the due date.

Learning Outcomes Assessed
  • Investigate and evaluate applications for data storage, management, retrieval, and analysis and visualisation
  • Apply knowledge to process data for data driven decision making
  • Analyse and generate solutions to solve data-related challenges
  • Demonstrate the knowledge required in using data science skills to solve business problems.


Graduate Attributes
  • Communication
  • Problem Solving
  • Critical Thinking
  • Information Literacy
  • Information Technology Competence
  • Ethical practice
  • Social Innovation

3 Presentation

Assessment Title
Presentation

Task Description

This assessment contributes to the Learning Outcomes 3, 4 and 5. This is an individual recorded presentation. The presentation topic is based on your report from assignment 2 and learning outcomes from this unit. You will need to record and submit a 5 to 7-minute video presentation explaining the key concepts. Your recorded video should include both the presenter and your desktop within the frame. Please ensure you adhere to appropriate and professional dress codes for your presentation.

 


Assessment Due Date

Week 12 Friday (7 Feb 2025) 11:45 pm AEST


Return Date to Students

Feedback and marks for this assessment will be released after the certification date as this unit does not have an exam.


Weighting
20%

Assessment Criteria

The assessment will be marked based on the following criteria:

Stay on topic
Fulfill requirements of topic
Quality of slides
Presentation style
Valid information presented

More details will be available on the Moodle site.


Referencing Style

Submission
Online

Submission Instructions
The presentation file must be submitted to Moodle by the due date.

Learning Outcomes Assessed
  • Apply knowledge to process data for data driven decision making
  • Analyse and generate solutions to solve data-related challenges
  • Demonstrate the knowledge required in using data science skills to solve business problems.


Graduate Attributes
  • Communication
  • Problem Solving
  • Information Literacy

Academic Integrity Statement

As a CQUniversity student you are expected to act honestly in all aspects of your academic work.

Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.

When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.

Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.

As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.

What is a breach of academic integrity?

A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.

Why is academic integrity important?

A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.

Where can I get assistance?

For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.

What can you do to act with integrity?