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Durham University

Vice-Chancellor's India Scholarships 2021/22

Data Science


Advances in many fields will be driven by those skilled in handling large parallel machines and data sets with extreme volume or velocity. Our MSc in Scientific Computing and Data Analysis (MISCADA) trains students in these areas, equips them with essential professional, entrepreneurial, and collaborative skills, and gives them the opportunity to apply all acquired knowledge to challenging, state-of-the-art problems from a computational specialisation area.


G5K609 Scientific Computing and Data Analysis MSc Postgraduate Taught  2020

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MSc
Mode of study Full Time
Duration 12 months
Start Date October
Location Durham City
More information Still have questions?
Department(s) Website www.durham.ac.uk/computer.science
Download Download as a PDF

Course Summary

Course Summary

Description

Advances in fields such as Physics, Engineering, Earth Sciences or Finance are increasingly driven by experts in computational techniques.  Notably, people skilled to write code for the most powerful computers in the world and skilled to process the biggest data sets in the world can truly make a difference. 

The MSc in Scientific Computing and Data Analysis offers an application-focused program to deliver these skills with three interwoven strands: 

  • Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage);
  • Mathematical aspects of data analysis;
  • Implementation and application of fundamental techniques in a domain specialisation (presently astrophysics, particle physics, or financial mathematics). 

 

Why study this course

This course will open doors for you, both in the industry as well as in further study, and aims to:

  1. Train the next generation of expert research-aware data and computational scientists and engineers for the global high tech sector, equipped with genuine understanding of the underlying computing technologies and methodologies
  2. Give you a deep insight into the state-of-the-art computational and data challenges in your chosen specialisation
  3. Enable you to bridge the widening gap between application domains, big data challenges and high-performance computing
  4. Prepare you to apply successfully for further higher education programmes (PhD) with a strong computing and data flavour in Durham or other world-leading institutions
  5. Make you aware of the societal, economical and ethical responsibilities, opportunities and implications tied to massive data processing and compute power; this includes training on entrepreneurship.

Watch our 1-minute course overview video here!

Chinese version here 

 

Course structure 

The course is structured into five modules spanning three terms and is currently available with a specialisation in astrophysics, particle physics, or financial mathematics.

In this course:

  1. you will obtain a strong baseline in methodological skills
  2. you will study selected topics from your chosen specialisation area with a strong emphasis on computational and data challenges.
  3. you can choose to put emphasis on data analysis or scientific computing 
  4. you will do a challenging project either within the methodological academic departments (Mathematical Sciences or Computer Science), or within the specialisation area, or in close cooperation with our industrial partners
  5. you will acquire important professional skills spanning collaboration and project management, presentation and outreach as well as entrepreneurial thinking

Learning and Teaching

Course Learning and Teaching

The course is taught using a wide range of learning research-led and teaching methods:

  • Lectures
  • Practical classes/computer labs
  • Independent study, research and analysis
  • Project (dissertation) and coursework
  • Group and individual presentations

A detailed list of learning and teaching methods is found per module in the module descriptions.

Besides the formal characteristics clarified in these descriptions, students from the course will be given the opportunity to work with a wide variety of top-notch computer kit and software:

  • GPGPU/heterogeneous architectures
  • HPC systems with specialist software installations (such as performance analysis tools)
  • GPU-based AI kit and data acquisition tools

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree (BSc) or equivalent

  • In Physics or a subject with basic physics courses OR
  • In Computer Science OR
  • In Mathematics OR
  • In any natural sciences with a strong quantitative element.

We strongly encourage students to sign up for a specialisation area for which they already have some background or affinity. At the moment, the course targets primarily Physics students and Mathematics students. If you do not have a degree from these subjects, we strongly recommend you to contact the University beforehand to clarify whether you bring along the right background. Please note that standard business degrees are not sufficient, as they lack the required level of mathematical education.

Programming knowledge on an L3 level in at least one programming language and commitment to learning C and Python independently if not known before. 

Interest in Computational Physics or its Data Analysis. The course tackles computational and data analysis challenges from this area.

Additional requirements

The course page provides self-assessment tests and tutorial links to assess your programming skills. We expect applicants to confirm themselves that they are aware of the required programming skills and provide evidence (course transcripts, links to programming projects or brief description of conducted projects).

English Language requirements - Band E (IELTS of 6.5 with no element below 6.0)

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

The tuition fees for 2020/21 academic year have not yet been finalised, they will be displayed here once approved.

The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

G5K609 Scientific Computing and Data Analysis MSc Postgraduate Taught  2021

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MSc
Mode of study Full Time
Duration 12 months
Start Date October
Location Durham City
More information Still have questions?
Department(s) Website www.durham.ac.uk/computer.science
Download Download as a PDF

Course Summary

Course Summary

Description

Advances in fields such as Physics, Engineering, Earth Sciences or Finance are increasingly driven by experts in computational techniques.  Notably, people skilled to write code for the most powerful computers in the world and skilled to process the biggest data sets in the world can truly make a difference. 

The MSc in Scientific Computing and Data Analysis offers an application-focused program to deliver these skills with three interwoven strands: 

  • Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage);
  • Mathematical aspects of data analysis;
  • Implementation and application of fundamental techniques in a domain specialisation (presently astrophysics, particle physics, or financial mathematics). 

 

Why study this course

This course will open doors for you, both in the industry as well as in further study, and aims to:

  1. Train the next generation of expert research-aware data and computational scientists and engineers for the global high tech sector, equipped with genuine understanding of the underlying computing technologies and methodologies
  2. Give you a deep insight into the state-of-the-art computational and data challenges in your chosen specialisation
  3. Enable you to bridge the widening gap between application domains, big data challenges and high-performance computing
  4. Prepare you to apply successfully for further higher education programmes (PhD) with a strong computing and data flavour in Durham or other world-leading institutions
  5. Make you aware of the societal, economical and ethical responsibilities, opportunities and implications tied to massive data processing and compute power; this includes training on entrepreneurship.

Watch our 1-minute course overview video here!

Chinese version here 

 

Course structure 

The course is structured into five modules spanning three terms and is currently available with a specialisation in astrophysics, particle physics, or financial mathematics.

In this course:

  1. you will obtain a strong baseline in methodological skills
  2. you will study selected topics from your chosen specialisation area with a strong emphasis on computational and data challenges.
  3. you can choose to put emphasis on data analysis or scientific computing 
  4. you will do a challenging project either within the methodological academic departments (Mathematical Sciences or Computer Science), or within the specialisation area, or in close cooperation with our industrial partners
  5. you will acquire important professional skills spanning collaboration and project management, presentation and outreach as well as entrepreneurial thinking

Learning and Teaching

Course Learning and Teaching

The course is taught using a wide range of learning research-led and teaching methods:

  • Lectures
  • Practical classes/computer labs
  • Independent study, research and analysis
  • Project (dissertation) and coursework
  • Group and individual presentations

A detailed list of learning and teaching methods is found per module in the module descriptions.

Besides the formal characteristics clarified in these descriptions, students from the course will be given the opportunity to work with a wide variety of top-notch computer kit and software:

  • GPGPU/heterogeneous architectures
  • HPC systems with specialist software installations (such as performance analysis tools)
  • GPU-based AI kit and data acquisition tools

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree (BSc) or equivalent

  • In Physics or a subject with basic physics courses OR
  • In Computer Science OR
  • In Mathematics OR
  • In any natural sciences with a strong quantitative element.

We strongly encourage students to sign up for a specialisation area for which they already have some background or affinity. At the moment, the course targets primarily Physics students and Mathematics students. If you do not have a degree from these subjects, we strongly recommend you to contact the University beforehand to clarify whether you bring along the right background. Please note that standard business degrees are not sufficient, as they lack the required level of mathematical education.

Programming knowledge on an graduate level in at least one programming language and commitment to learning C and Python independently if not known before. 

Interest in Computational Physics or its Data Analysis. The course tackles computational and data analysis challenges from this area.

Additional requirements

The course page provides self-assessment tests and tutorial links to assess your programming skills. We expect applicants to confirm themselves that they are aware of the required programming skills and provide evidence (course transcripts, links to programming projects or brief description of conducted projects).

 

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

Full Time Fees

EU Student £26,500.00 per year
Home Student £11,660.00 per year
Island Student £11,660.00 per year
International non-EU Student £26,500.00 per year

The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

G5K823 Master of Data Science MDS Postgraduate Taught  2020

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MDS
Mode of study Part Time + Full Time
Duration 1 year
Start Date October 2020
Location Durham City
More information Still have questions?
Department(s) Website
Download Download as a PDF

Course Summary

Course Summary

Description

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites.  Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result, has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science programme is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment.  The broad aims are:  

  • To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
  • To enable students to critically review and apply relevant data science knowledge to practical situations
  • To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
  • To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
  • To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
  • To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
  • To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
  • To extend the ability to communicate effectively both orally and in writing, using a range of media. 

The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline. 

A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate. 

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core Modules

The Master of Data Science programme is comprised of the following core modules:

  • Introduction to Computer Science
  • Introduction to Statistics for Data Science
  • Ethics and Bias in Data Analytics
  • Machine Learning
  • Programming for Data Science
  • Strategic Leadership
  • Introduction to Mathematics for Data Science
  • Research Project (60 credits)

Optional Modules available include:

  • Text Mining and Language Analytics
  • Data Exploration, Visualization, and Unsupervised Learning

Learning and Teaching

Course Learning and Teaching

The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science

Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments.  They are also developed through innovative teaching practices such as simulations.  Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.

 All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.

 The Master of Data Science uses a wide range of learning and teaching methods:

  • Lectures
  • Seminars
  • Workshops
  • Computer/practical classes
  • Independent study, research and analysis
  • Structured reading
  • Case studies
  • Data Science Project
  • Supervisions
  • Group and individual oral presentations

The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.

 Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.

Students also have an appointed Academic Advisor who is able to guide and inform them in their academic development and choice of optional modules.

Information, requirements and expectations regarding the programme overall are provided in the Programme Handbook, which is issued to all students at the beginning of the year and is available on Blackboard Ultra afterwards. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.

Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree or equivalent in a degree that excludes Mathematics and Physics and Computer Science.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

The tuition fees for 2020/21 academic year have not yet been finalised, they will be displayed here once approved.

The tuition fees shown are for one complete academic year of study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

G5K823 Master of Data Science MDS Postgraduate Taught  2021

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MDS
Mode of study Part Time + Full Time
Duration 1 year
Start Date October 2020
Location Durham City
More information Still have questions?
Department(s) Website
Download Download as a PDF

Course Summary

Course Summary

Description

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites.  Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result, has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science programme is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment.  The broad aims are:  

  • To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
  • To enable students to critically review and apply relevant data science knowledge to practical situations
  • To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
  • To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
  • To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
  • To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
  • To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
  • To extend the ability to communicate effectively both orally and in writing, using a range of media. 

The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline. 

A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate. 

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core Modules

The Master of Data Science programme is comprised of the following core modules:

  • Introduction to Computer Science
  • Introduction to Statistics for Data Science
  • Ethics and Bias in Data Analytics
  • Machine Learning
  • Programming for Data Science
  • Strategic Leadership
  • Introduction to Mathematics for Data Science
  • Research Project (60 credits)

Optional Modules available include:

  • Text Mining and Language Analytics
  • Data Exploration, Visualization, and Unsupervised Learning

Learning and Teaching

Course Learning and Teaching

The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science

Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments.  They are also developed through innovative teaching practices such as simulations.  Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.

 All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.

 The Master of Data Science uses a wide range of learning and teaching methods:

  • Lectures
  • Seminars
  • Workshops
  • Computer/practical classes
  • Independent study, research and analysis
  • Structured reading
  • Case studies
  • Data Science Project
  • Supervisions
  • Group and individual oral presentations

The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.

 Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.

Students also have an appointed Academic Advisor who is able to guide and inform them in their academic development and choice of optional modules.

Information, requirements and expectations regarding the programme overall are provided in the Programme Handbook, which is issued to all students at the beginning of the year and is available on Blackboard Ultra afterwards. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.

Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree or equivalent in a degree that excludes Mathematics and Physics and Computer Science.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

Full Time Fees

EU Student £24,900.00 per year
Home Student £10,500.00 per year
Island Student £10,500.00 per year
International non-EU Student £24,900.00 per year

The tuition fees shown are for one complete academic year of study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

G5K923 Master of Data Science (Digital Humanities) MDS Postgraduate Taught  2020

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MDS
Mode of study Full Time
Duration 1 year
Start Date October 2020
Location Durham City
More information Still have questions?
Department(s) Website
Download Download as a PDF

Course Summary

Course Summary

Description

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science (Digital Humanities) programme is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree in the Humanities.  Introductory modules are designed to bring students who are complete beginners and will require no prior knowledge of mathematics or programming  up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory.  Data Science core modules will include an introduction to mathematics for Data Science, statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks. 

In addition to that Data Science core, you will also take a module in Digital Humanities which will explore the application of quantitative and computational methods to cultural data: languages, literary, philosophical and theological texts, historical data, artifacts and material culture, visual art, video and music.  Alternatively, you may take a traditional MA module in your area of interest (subject to departmental approval and timetabling).

Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment.  The broad aims are:  

  • To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
  • To enable students to critically review and apply relevant data science knowledge to practical situations
  • To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
  • To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
  • To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
  • To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
  • To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
  • To extend the ability to communicate effectively both orally and in writing, using a range of media.

 The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline.

 A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue where you will apply the techniques you have learned from your Data Science modules to a research problem in a Humanities domain of your choosing.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core Modules

The Master of Data Science (Digital Humanities) programme is comprised of the following core modules:

  • Introduction to Computer Science
  • Introduction to Statistics for Data Science
  • Machine Learning
  • Programming for Data Science
  • Introduction to Mathematics for Data Science
  • Digital Humanities: Theory and Practice
  • Research Project (60 credits)

Optional Modules available include: 

  • Ethics and Bias in Data Analytics
  • Text Mining and Language Analytics
  • Data Exploration, Visualization, and Unsupervised Learning
  • Strategic Leadership

Learning and Teaching

Course Learning and Teaching

The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science.

Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments.  They are also developed through innovative teaching practices such as simulations.  Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.

All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.

The Master of Data Science uses a wide range of learning and teaching methods:

  • Lectures
  • Seminars
  • Workshops
  • Computer/practical classes
  • Independent study, research and analysis
  • Structured reading
  • Case studies
  • Data Science Project
  • Supervisions
  • Group and individual oral presentations

The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.

 

Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.

Students also have an appointed Academic Advisor who is able to guide and inform them in their academic development and choice of optional modules.

Information, requirements and expectations regarding the programme overall are provided in the Programme Handbook, which is issued to all students at the beginning of the year and is available on Blackboard Ultra afterwards. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.

Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree or equivalent in a degree in the Humanities.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

The tuition fees for 2020/21 academic year have not yet been finalised, they will be displayed here once approved.

The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

G5K923 Master of Data Science (Digital Humanities) MDS Postgraduate Taught  2021

Essentials

Essentials

Please note: 2020-21 courses may be affected by Covid-19 and are therefore subject to change due to the ongoing impact of Covid-19. Summaries of course-specific changes resulting from the impact of Covid-19 will be provided to applicants during August 2020.

For the latest information on our plans for teaching in academic year 2020/21 in light of Covid-19, please see www.durham.ac.uk/coronavirus

Degree MDS
Mode of study Part Time + Full Time
Duration 1 year
Start Date October 2020
Location Durham City
More information Still have questions?
Department(s) Website
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Course Summary

Course Summary

Description

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science (Digital Humanities) programme is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree in the Humanities.  Introductory modules are designed to bring students who are complete beginners and will require no prior knowledge of mathematics or programming  up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory.  Data Science core modules will include an introduction to mathematics for Data Science, statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks. 

In addition to that Data Science core, you will also take a module in Digital Humanities which will explore the application of quantitative and computational methods to cultural data: languages, literary, philosophical and theological texts, historical data, artifacts and material culture, visual art, video and music.  Alternatively, you may take a traditional MA module in your area of interest (subject to departmental approval and timetabling).

Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment.  The broad aims are:  

  • To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
  • To enable students to critically review and apply relevant data science knowledge to practical situations
  • To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
  • To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
  • To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
  • To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
  • To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
  • To extend the ability to communicate effectively both orally and in writing, using a range of media.

 The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline.

 A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue where you will apply the techniques you have learned from your Data Science modules to a research problem in a Humanities domain of your choosing.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core Modules

The Master of Data Science (Digital Humanities) programme is comprised of the following core modules:

  • Introduction to Computer Science
  • Introduction to Statistics for Data Science
  • Machine Learning
  • Programming for Data Science
  • Introduction to Mathematics for Data Science
  • Digital Humanities: Theory and Practice
  • Research Project (60 credits)

Optional Modules available include: 

  • Ethics and Bias in Data Analytics
  • Text Mining and Language Analytics
  • Data Exploration, Visualization, and Unsupervised Learning
  • Strategic Leadership

Learning and Teaching

Course Learning and Teaching

The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science.

Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments.  They are also developed through innovative teaching practices such as simulations.  Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.

All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.

The Master of Data Science uses a wide range of learning and teaching methods:

  • Lectures
  • Seminars
  • Workshops
  • Computer/practical classes
  • Independent study, research and analysis
  • Structured reading
  • Case studies
  • Data Science Project
  • Supervisions
  • Group and individual oral presentations

The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.

 

Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.

Students also have an appointed Academic Advisor who is able to guide and inform them in their academic development and choice of optional modules.

Information, requirements and expectations regarding the programme overall are provided in the Programme Handbook, which is issued to all students at the beginning of the year and is available on Blackboard Ultra afterwards. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.

Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.

Apply

Admissions Process

Subject requirements, level and grade

A UK first or upper second class honours degree or equivalent in a degree in the Humanities.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English Language requirements

Please check requirements for your subject and level of study.

How to apply

www.durham.ac.uk/postgraduate/apply

Fees and Funding

Fees and Funding

Full Time Fees

EU Student £24,900.00 per year
Home Student £10,500.00 per year
Island Student £10,500.00 per year
International non-EU Student £24,900.00 per year

The tuition fees shown are for one complete academic year of study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and funding

www.durham.ac.uk/postgraduate/finance

Open Days and Visits

Open days and visits

Pre-application open day

www.durham.ac.uk/postgraduate/visit

Overseas Visit Schedule

www.durham.ac.uk/international/office/meetus

Postgraduate Visits

PGVI or

www.durham.ac.uk/postgraduate/visit/

More information coming soon.

More information coming soon.


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