Master of Applied Data Science

You must complete three foundational studies units.

Introduction to databases

Introduction to databases

Unit Code: FIT9132

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Database management systems are integral to the success of organisations. ‘Introduction to databases’ serves as a thorough introduction to database management, built upon a theoretical foundational of relational modelling, analysis and design. You will learn all about the core principles of sound database design, and how to implement relational databases by employing an SQL approach.

Algorithms and programming foundations in Python

Algorithms and programming foundations in Python

Unit Code: FIT9136

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Python is the industry-standard programming language for data science, ideally suited to implementing algorithms in data analysis. ‘Algorithms and programming foundations in Python’ introduces the data structures and algorithms widely used in modern programming language, and how they can be used to solve simple problems. You will also cover data science-relevant topics including IOs, control structures, and object-oriented design principles such as lists, stacks, queues, trees, recursion, and searching/sorting algorithms.

Mathematical foundations for data science and AI

Mathematical foundations for data science and AI

Unit Code: MAT9004

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Data science draws on a foundation of strong mathematical principles. In ‘Mathematical foundations for data science and AI’ you will acquire a practical understanding of mathematical topics fundamental to computing and statistics, including: trees and other graphs, counting in combinatorics, principles of elementary probability theory, linear algebra, and fundamental concepts of calculus in one and several variables.

You must complete these three core studies units.

Introduction to data science

Introduction to data science

Unit Code: FIT5145

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Working with data means different things in different organisational contexts, which is why it’s vital for data science professionals to have an adaptable toolkit at their disposal. ‘Introduction to Data Science’ teaches various ways of working with data to draw actionable insights. You will acquire a versatile data science toolkit grounded in real world practice – exploring industry-relevant case studies as well as archival and architectural practice, taking into account policy, legal and ethical issues. You will learn skills related to data handling, including: data collection and storage; data preparation and cleaning; and data stream processing.

Data wrangling

Data wrangling

Unit Code: FIT5196

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Data wrangling is an important aspect of data science, and essential when converting raw data into actionable insights that align with an organisation’s needs. ‘Data wrangling’ foregrounds the problems that prevent raw data from being effectively used in analytics, and how rigorous data cleanings and pre-processing is fundamental to preparing data for analytics. You will gain practical skills – conducted through Python and the Pandas environment – in handling bad and missing data, data integration, initial feature selection, text mining and web analytics.

Statistical data modelling

Statistical data modelling

Unit Code: FIT5197

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

The basis of data science is mathematical, in particular statistical modelling. In ‘Statistical data modelling’ this aspect of data analysis is given pride of place, with a focus on how modelling is used in a wide range of professional contexts. By working through case studies and industry-relevant examples, you will learn about analytic tasks, including: statistical hypothesis testing and exploratory versus confirmatory analysis. Similarly, you will acquire skills in a range of concepts fundamental to data modelling and prediction, including: basic probability distributions; random number generation and simulation; and estimation methods such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation.

You must complete four specialist studies units.

Data exploration and visualisation

Data exploration and visualisation

Unit Code: FIT5147

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

‘Data exploration and visualisations’ introduces statistical and visualisation techniques for the exploratory analysis of data. You will cover the role of data visualisation in data science and its limitations, including discussion of qualitative and quantitative visualisation, as well as temporal and spatial data, and additional topics in interactive data visualisation, and creating data visualisations with R and other tools.

Applied data analysis

Applied data analysis

Unit Code: FIT5149

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

‘Applied Data Analysis’ examines data analysis in its professional context, and is all about introducing contemporary methodologies such as Machine Learning techniques, ensuring students have the ability to present data effectively using widely available analysis software. You will investigate a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms, including: regression, classification, clustering and so on.

Machine learning

Machine learning

Unit Code: ITO5201

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Machine learning is, in many ways, already revolutionising the broad ICT sector. In ‘Machine Learning’ you will explore the impact machine learning is having on the major statistical learning models and algorithms used in data analysis. You will build your understanding of these modelling algorithms – contextualised according to the emergence and evolution of machine learning – based upon foundational concepts, including: linear models for regression and classification (logistic regression, Bayesian classifiers), discriminative and generative models, k-means and latent variable models (Gaussian mixture model), expectation-maximisation, neural networks and deep learning.

Data processing for big data

Data processing for big data

Unit Code: FIT5202

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

When the amount of data scales up, the methodology behind data processing must do so as well. ‘Data processing for big data’ focuses on big data processing, including volume, complexity, and velocity using the latest big data technologies. You will learn about large volume data processing using parallel technologies, and data streaming processing, both methodologies essential to processing large amounts of data efficiently.

Data in society

Data in society

Unit Code: ITO5205

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

The explosion of data and discovery are transforming the way we live and work, altering relationships between government and citizens, businesses and consumers, the researchers and the researched, the public and private sectors, the individual and society. This unit explores how big data analytics can potentially help grow the economy, improve health and education, support national security, protect the environment, enable more energy efficiency, drive innovation and progress and sustainable communities and cultures. It also addresses the risks associated with the application of big data analytics in government and big business to support greater surveillance of citizen by state and consumers by business, disempower individuals and vulnerable communities, support discrimination, threaten social inclusion, social justice, human and civil rights, self-determination and privacy, and widen the divide between the data haves and have-nots.

Data analysis for semi-structured data

Data analysis for semi-structured data

Unit Code: FIT5212

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

Semi-structured data is one of the fastest growing kinds of data in both the public and private sector. Email collections with sender-recipient graphs, metadata and text content is one example. ‘Data analysis for semi-structured data’ will explore basic forms of semi-structured data: text, time-sequence data, graphs and multiple relations in a database. You will learn to apply basic machine learning algorithms – and methodologies such as cohort analysis and market-based analysis – to solve industry problems for the application of semi-structured data.

Applied practice 1

Applied practice 1

Unit Code: ITO5001

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

The role of IT professionals is constantly evolving in the face of global technological change. ‘Applied practice 1’ provides you with a practical and theoretical understanding of what being a modern IT professional means, and how the role adapts. You will discuss issues relating to professional practice in the workplace, including the ethical and legal challenges of technology.

Applied practice 2

Applied practice 2

Unit Code: ITO5002

Contact Hours: 20-24 hours of study per week

Duration: 6 weeks

Description:

In ‘Applied practice 2’ you will have the chance to research contemporary approaches to developing a project. This unit is an opportunity for you to apply the skills and knowledge you have already gained in a practical setting. You will research and create a specific problem or question and then develop a solution according to industry standards for use by organisations or community groups.