Data Science Case Studies

Unit Outline (Higher Education)

   
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Effective Term: 2024/05
Institute / School :Institute of Innovation, Science & Sustainability
Unit Title: Data Science Case Studies
Unit ID: ITECH7003
Credit Points: 15.00
Prerequisite(s): (ITECH5007)
Co-requisite(s): Nil
Exclusion(s): Nil
ASCED: 020199
Other Change:  
Brief description of the Unit

DATA SCIENCE CASE STUDIES will focus on the application of data science techniques/tools to various domains (real-world data). It uses analytical and data science methods to solve real-world application questions and to implement the solution using tools. We will work through case studies in a variety of contexts including, e.g., business, science, healthcare, industry, education and society to investigate how knowledge and value are extracted from data. Through examining the wide-ranging applications of data science, we will further understand the underlying learning algorithms, models, codes and data. Topics will include experimental and project design, business predictive analytics, data processing, model training and evaluation, algorithm and code analysis, application cases analytics, software tools, visualisation and project management.

Grade Scheme: Graded (HD, D, C, P, MF, F, XF)
Work Experience Indicator:
No work experience
Placement Component:
Supplementary Assessment:Yes
Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment
Course Level:
Level of Unit in CourseAQF Level(s) of Course
5678910
Introductory                                                
Intermediate                                        
Advanced                                                
Learning Outcomes:
Knowledge:
K1.

Contrast data science applications in various domains.

K2.

Examine basic principles that underpin data science, experimental design, algorithms, and learning models.

K3.

Integrate knowledge of data science and associated tools for developing authentic data science projects.

K4.

Analyse, evaluate and synthesise findings from data science investigations in a form suitable for specialist and non-specialist audiences.

Skills:
S1.

Critically evaluate the keys to successful data science project implementation.

S2.

Effectively apply data science knowledge and techniques to solve authentic problems.

S3.

Design and execute a data science project based on business requirements, and reflect on the experience.

Application of knowledge and skills:
A1.

Utilise modelling, analysis, programming, and visualisation techniques/tools for data science projects.

A2.

Select and employ relevant standards, ethical and social considerations in the analysis of a real-world scenario of data science practice in industry.

Unit Content:

Topics may include: experimental and project design data design predictive analytics data processing model training and evaluation algorithm and code analysis application case analytics data visualisation practice software tools project management ethical and social considerations

Graduate Attributes:
 Learning Outcomes AssessedAssessment TasksAssessment TypeWeighting
1.

K1-4, S1-3, A1

Develop skills in the analysis and practical application of data science techniques/tools.

Tutorials, assignments, and/or exercises

40%-60%

2.

K1-4, S1-3, A1-A2

Students will provide theoretical answers and provide practical solutions to a range of questions and problems drawn from case studies.

Test(s)

40%-60%

Adopted Reference Style:
APA  ()

Professional Standards / Competencies:
 Standard / Competency