Data Science, _MSc

Stage 1

Compulsory Courses

Introduction to Programming (PX5007)

15 Credit Points

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

Detailed course information

Introduction to Data Science (PX5008)

15 Credit Points

In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.

We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.

Detailed course information

Statistics and Time Series Analysis (PX5010)

15 Credit Points

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

Detailed course information

Getting Started at the University of Aberdeen (PD5006)

This course, which is prescribed for all taught postgraduate students, is studied entirely online, takes approximately 5-6 hours to complete and can be taken in one sitting, or spread across a number of weeks.

Topics include orientation overview, equality and diversity, health, safety and cyber security and how to make the most of your time at university in relation to careers and employability.

Successful completion of this course will be recorded on your Enhanced Transcript as ‘Achieved’.

Detailed course information

Optional Courses

Select ONE of the following:

Machine Learning (PX5009)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

Detailed course information

Machine Learning (CS5062)

15 Credit Points

This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.

Detailed course information

Stage 2

Compulsory Courses

Advanced Statistics and Special Applications (PX5504)

15 Credit Points

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

Detailed course information

Audio, Image and Video Analysis (PX5505)

15 Credit Points

This course introduces the tools needed to analyse audio recordings, images and videos.

It will contain aspects of image enhancements, content detection, segmentation analysis (e.g. detecting tumours in medical imaging data), handwritten character recognition, subtitle analysis, and many other techniques.

Detailed course information

Case Studies in Data Science (PX5506)

15 Credit Points

This course brings together all aspects of data science, from gathering data, to analysis and visualisation, by exploring real world applications of data science. There will be a discussion of some significant achievements of data science when applied to various areas from fundamental business practices to physics. Students will then apply these skills to a group project.

Detailed course information

Optional Courses

Select ONE of the following:

CS551G - Data Mining and Visualisation (15 credit points)

Data Visualisation (PX5503)

15 Credit Points

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

Detailed course information

Stage 3

Compulsory Courses

Data Science Project (PX5901)

60 Credit Points

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner.

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
EU / International students	£27,000
Tuition Fees for 2023/24 Academic Year
Home / RUK	£12,154
Tuition Fees for 2023/24 Academic Year

Stage 1

Compulsory Courses

Introduction to Programming (PX5507)

15 Credit Points

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

Detailed course information

Introduction to Data Science (PX5508)

15 Credit Points

The goal of this course is to introduce the student into the field of data science. You will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using it. You will learn the tools and techniques necessary to obtain the data, store it and manipulate it. You will learn tools and techniques to preprocess it and prepare it for analysis, statistical characterization and visualization. Then, you will be introduced to simple modelling techniques aimed at providing answers for the problems you want to solve. The final lectures are dedicated to introduce the MySQL and Mongo relational and non-relational databases, respectively.

Detailed course information

Introduction to Python and R (PX5526)

15 Credit Points

In this course we will introduce Python and R for the MSc Data Science. This will include common packages used for data science and we will discuss typical programming constructs used in data science.

Detailed course information

Statistics and Time Series Analysis (PX5510)

15 Credit Points

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

Detailed course information

Getting Started at the University of Aberdeen (PD5506)

This course, which is prescribed for all taught postgraduate students, is studied entirely online, takes approximately 5-6 hours to complete and can be taken in one sitting, or spread across a number of weeks.

Topics include orientation overview, equality and diversity, health, safety and cyber security and how to make the most of your time at university in relation to careers and employability.

Successful completion of this course will be recorded on your Enhanced Transcript as ‘Achieved’.

Detailed course information

Optional Courses

Plus one of the following:

Stage 2

Compulsory Courses

Data Visualisation (PX5019)

15 Credit Points

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

Detailed course information

Advanced Statistics and Special Applications (PX5020)

15 Credit Points

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

Detailed course information

Machine Learning (PX5009)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

Detailed course information

Image Analysis (PX5023)

15 Credit Points

Nowadays a large volume of data is stored in form of images. This course introduces the tools needed to analyse images and extract information from them, including aspects of image enhancement, filtering, segmentation, morphological analysis and image classification based on convolutional neural networks.

Detailed course information

Stage 3

Compulsory Courses

Data Science Project (PX55PA)

60 Credit Points

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner.

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
EU / International students	£27,000
Tuition Fees for 2023/24 Academic Year
Home / RUK	£12,154
Tuition Fees for 2023/24 Academic Year

Stage 1

Compulsory Courses

Introduction to Programming (PX5515)

15 Credit Points

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

Detailed course information

Introduction to Data Science (PX5524)

15 Credit Points

In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.

We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.

Detailed course information

Machine Learning (PX5517)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

Detailed course information

Statistics and Time Series Analysis (PX5518)

15 Credit Points

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

Detailed course information

Stage 2

Compulsory Courses

Data Visualisation (PX5011)

15 Credit Points

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

Detailed course information

Advanced Statistics and Special Applications (PX5012)

15 Credit Points

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

Detailed course information

Audio, Image and Video Analysis (PX5013)

15 Credit Points

This course introduces the tools needed to analyse audio recordings, images and videos.

It will contain aspects of image enhancements, content detection, segmentation analysis (e.g. detecting tumours in medical imaging data), handwritten character recognition, subtitle analysis, and many other techniques.

Detailed course information

Case Studies in Data Science (PX5014)

15 Credit Points

This course brings together all aspects of data science, from gathering data, to analysis and visualisation, by exploring real world applications of data science. There will be a discussion of some significant achievements of data science when applied to various areas from fundamental business practices to physics. Students will then apply these skills to a group project.

Detailed course information

Stage 3

Compulsory Courses

Data Science Project (PX55PB)

60 Credit Points

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner.

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
All Students	£1,280
Modular fee programmes - Tuition fees for 2021/22 academic year. This programme is charged on a modular basis. The fee quoted is per 15 credit points. Fee does not apply to the Individual Project, which is quoted below.
All Students	£3,040
Project - Academic year 2022/23. This is the fee charged for the Individual Project (60 credits) which is taken at the end of the programme.
Indicative Programme Fee	£13,540
Indicative programme fee based on studies beginning in the 2021/22 academic year. Fees increment annually. This fee is calculated on the basis of part-time study of 180 credits of study over 3 years.

Stage 1

Compulsory Courses

Introduction to Programming (PX5015)

15 Credit Points

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

Detailed course information

Introduction to Data Science (PX5016)

15 Credit Points

In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.

We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.

Detailed course information

Machine Learning (PX5017)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

Detailed course information

Statistics and Time Series Analysis (PX5018)

15 Credit Points

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

Detailed course information

Stage 2

Compulsory Courses

Data Visualisation (PX5511)

15 Credit Points

Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.

Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.

Detailed course information

Advanced Statistics and Special Applications (PX5512)

15 Credit Points

In this module we will discuss advanced and cutting-edge statistical tools and techniques.

Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.

Detailed course information

Audio, Image and Video Analysis (PX5513)

15 Credit Points

This course introduces the tools needed to analyse audio recordings, images and videos.

It will contain aspects of image enhancements, content detection, segmentation analysis (e.g. detecting tumours in medical imaging data), handwritten character recognition, subtitle analysis, and many other techniques.

Detailed course information

Case Studies in Data Science (PX5514)

15 Credit Points

This course brings together all aspects of data science, from gathering data, to analysis and visualisation, by exploring real world applications of data science. There will be a discussion of some significant achievements of data science when applied to various areas from fundamental business practices to physics. Students will then apply these skills to a group project.

Detailed course information

Stage 3

Compulsory Courses

Data Science Project (PX5902)

60 Credit Points

This is a project course for the MSc in Data Science. Students will be given a data science project, which will be supervised by one member of staff. Students will conduct research on that topic in an independent manner.

Students will have to deliver a presentation halfway through the project and hand in a report about the results at the end of the project. This will be followed by an oral examination of the submitted report.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
All Students	£1,280
Modular fee programmes - Tuition fees for 2021/22 academic year. This programme is charged on a modular basis. The fee quoted is per 15 credit points. Fee does not apply to the Individual Project, which is quoted below.
All Students	£2,900
Project - Academic year 2021/22. This is the fee charged for the Individual Project (60 credits) which is taken at the end of the programme.
Indicative Programme Fee	£13,140
Indicative programme fee based on studies beginning in the 2021/22 academic year. This fee is calculated on the basis of full-time study of 180 credits of study over 1 year.