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University of Oxford: Social Data Science
| Institution | University of Oxford |
|---|---|
| Department | Engineering Science |
| Web | https://www.ox.ac.uk |
| graduate.admissions@admin.ox.ac.uk | |
| Telephone | +44 (0)1865 270059 |
DPhil
Summary
**The information provided on this page was correct at the time of publication (November 2024). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (November 2024). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (November 2023). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (November 2023). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (October/November 2021). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
The multidisciplinary DPhil in Social Data Science provides opportunity for highly qualified students to undertake cutting- edge research focused on using unstructured heterogeneous data about human behaviour in a theoretically informed manner, thereby advancing our understanding of social processes.
Social traces - often generated digitally from, for example, social media, communications platforms, devices, sensors/wearables, and mobile phones - offer a way to accumulate new large-scale data, in addition to existing archives and repositories that have been converted to digital formats. Such data can be put to work helping us understand old and new foundational problems of crucial interest to the social sciences, industry, and policy-makers including social, economic and political behaviour, interpersonal relationships, market design, group formation, identity, international movement, ethics and responsible ways to enhance the social value of data, and many other topics.
The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real world data; and developing the tools and techniques to analyse them to tell us something about the world, through explanation, prediction and the testing of interventions. In this way, social data science offers a data science where the data relates to individual and social behaviour and a theoretically informed social science with generation and analysis of real-time transactional data at its centre.
Over the course of the three to four years, you are expected to produce an important and original piece of scholarship that will make a significant contribution to the dynamic area of Social Data Science. On completion, you will have the qualities and transferable skills necessary to excel in teaching, research, policymaking or business.
The DPhil programme at the Oxford Internet Institute (OII) is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. The part-time degree offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in high tech industries to undertake rigorous long-term research that may be relevant to their working life. Please visit the department website for further details on part-time doctoral study or contact admissions@oii.ox.ac.uk.
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (October/November 2021). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
The multidisciplinary DPhil in Social Data Science provides opportunity for highly qualified students to undertake cutting- edge research focused on using unstructured heterogeneous data about human behaviour in a theoretically informed manner, thereby advancing our understanding of social processes.
Social traces - often generated digitally from, for example, social media, communications platforms, devices, sensors/wearables, and mobile phones - offer a way to accumulate new large-scale data, in addition to existing archives and repositories that have been converted to digital formats. Such data can be put to work helping us understand old and new foundational problems of crucial interest to the social sciences, industry, and policy-makers including social, economic and political behaviour, interpersonal relationships, market design, group formation, identity, international movement, ethics and responsible ways to enhance the social value of data, and many other topics.
The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real world data; and developing the tools and techniques to analyse them to tell us something about the world, through explanation, prediction and the testing of interventions. In this way, social data science offers a data science where the data relates to individual and social behaviour and a theoretically informed social science with generation and analysis of real-time transactional data at its centre.
Over the course of the three to four years, you are expected to produce an important and original piece of scholarship that will make a significant contribution to the dynamic area of Social Data Science. On completion, you will have the qualities and transferable skills necessary to excel in teaching, research, policymaking or business.
The DPhil programme at the Oxford Internet Institute (OII) is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. The part-time degree offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in high tech industries to undertake rigorous long-term research that may be relevant to their working life. Please visit the department website for further details on part-time doctoral study or contact admissions@oii.ox.ac.uk.
| Study type | Research |
|---|---|
| Level | RQF Level 8 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
MSc
Summary
**The information provided on this page was correct at the time of publication (November 2024). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 7 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (November 2023). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied program that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary terms, this equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
**DPhil**
The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.
**For the full descriptions, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas**
| Study type | Research |
|---|---|
| Level | RQF Level 7 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
Summary
**The information provided on this page was correct at the time of publication (November 2022). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**
The multidisciplinary MSc in Social Data Science provides the social and technical expertise needed to collect, critique, and analyse unstructured data about human behaviour.
This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes. For more information see the full details about this pilot.
The growing field of social data science sits at the intersection of data science approaches to information retrieval, modelling, and prediction with social science approaches to theory-driven analysis, critiques of social processes, and linkages between policy and practice. The Social Data Science degree seeks students with training or a demonstrable aptitude for social science work and programming to refine and extend their skills through the generation, analysis, and critique of large-scale social data. The tools for such an approach are multifaceted and evolve quickly. Our programme embeds recent machine learning approaches to prediction, scalable strategies for ingesting and managing large scale data, analytical statistics for explanations, and specialist approaches such as computer vision, natural language processing, and network science. As a social science degree these approaches are generally applied to questions of social scientific relevance such as social inequality, censorship, hate speech, cohesion, and wellbeing.
Students will be expected to spend around 40 hours studying each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary Terms, MSc students are advised to allocate between 10 and 15 hours each week for each course they undertake.
| Study type | Taught |
|---|---|
| Level | RQF Level 7 |
| Entry requirements | For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas |
| Location | University of Oxford University Offices Wellington Square Oxford OX1 2JD |
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