DATA SCIENCE PROJECTS

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Overview

In this capstone subject, you will work in teams to investigate and solve two problems based on a real-world application of data science. In the first project, you will design an optimisation tool and develop it as a web application in the R Shiny environment. The second project requires the team to develop a neural network model developed in the R programming environment using a training data set. This subject addresses the Innovation and Entrepreneurship La Trobe Essential, and requires you to think creatively, work in a team and present your ideas and results clearly and effectively.

Portfolio

Office of the Provost

School

Subject coordinator

Chris Taylor

Subject type

Undergraduate

Year level

Year Level 3 - UG

AQF level

Level 7 - Bachelor Degree

Available as elective

Available to study abroad / exchange students

Yes

Capstone subject
Capstones provide students with a way of integrating and applying knowledge and skills gained throughout their course.

Yes

Academic progress review - Schedule A subject

Subject instances

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Career ready

Career focused:This subject contains career development learning or professional engagement opportunities

Work based learning (placement):No

Graduate capabilities

COMMUNICATION - Communicating and Influencing

DISCIPLINE KNOWLEDGE AND SKILLS

INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving

INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry

PERSONAL AND PROFESSIONAL - Adaptability and Self-Management

Subject intended learning outcomes

On successful completion you will be able to:

Work effectively and efficiently as a member of a team to deliver solutions to real-world problems.

Design and develop an online, user-friendly tool that produces solutions for constrained optimisation problems.

Construct a model that can be used to predict outcomes with high accuracy for unclassified data.

Explain use of the developed tools and models verbally and in writing, to a general audience.

Implement appropriate testing strategies to ensure robustness of the developed tools and models.

Handbook