Data Science (with specialisation in Statistics) MSc, PGDip, PGCert

Program Overview

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Overview

This course blends modern statistical methods with the computational skills to handle large quantities of unstructured data.

Data Science is revolutionising every area of science, engineering and commerce. It has potential for huge societal and economic benefits. Statistical analysis, with data analytics, is

in-demand

. There is an industry need for analytical skills that can

interpret and extract value from complex data

.

During this course you'll develop a comprehensive understanding of

data science theory

, and how to use it in real world scenarios. You'll also develop a combination of skills in statistics and computer science.

You’ll put your new skills into action immediately, and

work with real data sets

in your applied data science projects. All module assessments are based on these projects.

We focus on a wide range of application areas, including:

healthcare

transport

cybersecurity

smart cities

manufacturing

This data science course is part of the following suite:

Data Science MSc, PGDip, PGCert

Data Science (with specialisation in Artificial Intelligence)

Data Science (with Specialisation in Visualization), MSc, PGDip, PGCert

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Important information

We've highlighted important information about your course. Please take note of any deadlines.

Your course and study experience - disclaimers and terms and conditions

Please rest assured we make all reasonable efforts to provide you with the programmes, services and facilities described. However, it may be necessary to make changes due to significant disruption, for example in response to Covid-19.

View our Academic experience page, which gives information about your Newcastle University study experience for the academic year 2022-23.

See our terms and conditions and student complaints information, which gives details of circumstances that may lead to changes to programmes, modules or University services.

Program Outline

What you'll learn

The course has three phases.

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Phase one

We’ll introduce you to the

core knowledge and skills

in statistics and computer science.

You’ll also receive introductory

training in Python

, alongside

data science modules

in:

data visualisation

machine learning

time series analysis

Bayesian inference

These modules are taught as an intensive block. This means that

full-time students

are taught two modules simultaneously. Teaching for part-time students is timetabled to accommodate everyone.

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Phase two

Phase two consists of more

advanced technical modules

, as well as a

group project

.

We'll introduce the aspects that govern all areas of data science practice, including:

professionalism

legislation

ethics

During the group project, you'll develop and evaluate a data science solution to a complex,

real-world problem

. This is in collaboration with industry members.

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Phase three

Phase three is an

individual research and development project

.

You'll receive personal supervision in one of our Schools research labs. This is in collaboration with industry, or with your current employer.

This MSc forms part of the following suite of data science courses:

Data Science MSc, PGDip, PGCert

Data Science (with specialisation in Artificial Intelligence)

Data Science (with specialisation in Visualization) MSc, PGDip, PGCert

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Modules

You will study modules on this course. A module is a unit of a course with its own approved aims and outcomes and assessment methods.

Course content changes

Module information is intended to provide an example of what you will study.

Our teaching is informed by research. Course content changes periodically to reflect developments in the discipline, the requirements of external bodies and partners, and student feedback.

Full details of the modules on offer will be published through the Programme Regulations and Specifications ahead of each academic year. This usually happens in May.

Optional modules availability

Some courses have optional modules. Student demand for optional modules may affect availability.

To find out more please see our terms and conditions.

Data Science (Statistics) MSc modules

Compulsory Modules	Credits
Engineering for AI	10
Computing Foundations of Data Science	10
Data Visualization	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild	10
Group Project in Data Science	10
Project and Dissertation in Data Science	80
Time Series Data	10
Statistical Learning Methodology	10
Bayesian Methodology	10

If you have permission from the Degree Programme Director, you can swap Engineering for AI to take one elective module from the following list:

Complex Data Visualization

Deep Learning

Data and Truth

Optional Modules	Credits
Image Informatics	10
Machine Learning with Project	10

Data Science (Statistics) PGDip modules

Compulsory Modules	Credits
Engineering for AI	10
Computing Foundations of Data Science	10
Data Visualization	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild	10
Group Project in Data Science	10
Diploma Project and Dissertation in Data Science	20
Time Series Data	10
Statistical Learning Methodology	10
Bayesian Methodology	10

If you have permission from the Degree Programme Director, you can swap Engineering for AI to take one elective module from the following list:

Complex Data Visualization

Deep Learning

Data and Truth

Optional Modules	Credits
Image Informatics	10
Machine Learning with Project	10

Data Science (Statistics) PGCert modules

Optional Modules	Credits
Engineering for AI	10
Data Visualization	10
Image Informatics	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild	10
Group Project in Data Science	10
Cloud Computing with Project	10
Machine Learning with Project	10
Complex Data Visualization	10
Statistical Foundations of Data Science	10
Statistical Learning for Data Science	10

If you have permission from the Degree Programme Director, you can swap Engineering for AI to take one elective module from the following list:

Complex Data Visualization

Deep Learning

Data and Truth