Online course

Data Analytics and Social Statistics

  • Qualification: MSc, PGDip
  • Duration: 18-27 months, part-time
  • Workload: Approx 20 hours per week
  • Next enrolment: September 2023
  • Fees: MSc £15,500; PGDip £10,333
hand on laptop with data and graphs

Introduction

Learn to master social data

The field of data analytics is developing rapidly. With the rise of ever larger and more specialised datasets, it’s essential to understand how to collect, handle, evaluate and interpret data to unleash its true potential.

Through studying this fully online, part-time course, you will learn to process and analyse complex social data effectively, improving your skills and professional outcomes in the process.

Leveraging real-world data and R software, this practical course will ensure you learn applicable techniques to take into the workplace.

Key features

Applied and practical learning

Use of real-world data and free R software to match day-to-day work scenarios.

Research and teaching excellence

Trusted by worldwide students and supported by high profile academics.

Latest methods and techniques

Interdisciplinary approach covering latest methods such as machine learning.

Logo

Real-world data and interdisciplinary expertise

  • Learn the tools and hone the techniques you will use in day-to-day data analysis work
  • Practically apply your learning to current and developing trends and topics across disciplines
  • Work closely with course colleagues from diverse professional and global backgrounds

Key information

  • Delivery

    100% online learning

  • Duration

    MSc - 27 months, part-time
    PGDip - 18 months, part-time

  • Qualification

    MSc (180 credits) - to achieve a Master of Science, you need to complete six 20-credit units, a mandatory 20-credit RSiP unit, and a 40-credit dissertation project
    PGDip (120 credits) - to achieve a PG Diploma, you need to complete six 20-credit units

  • Enrolment date

    September

  • Apply

    Please see our advice to applicants before applying

  • Workload

    Approx 20 hours per week

  • Course director

    Dr Alexandru Cernat, Senior Lecturer in the Department of Social Statistics and the chair of the Social Statistics Section of the Royal Statistical Society

Fees and funding

September 2023 tuition fees (UK/EU/International):

  • MSc, £15,500
  • PGDip, £10,333

You can save up to 15% on your tuition fees. Please see our fees and funding section below for more details.

We offer payment by instalments , so you can spread the cost of studying with us.

Explore a range of scholarships and bursaries available for this course below.

Find out more about fees and funding

Entry requirements

An Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline.

We may also consider exceptional applicants with a Lower Second (2:2) class honours degree if you have research experience or equivalent professional experience.

Find out more about entry requirements

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Course overview

Who this course is for

If you are interested in upskilling and discovering the power of data for predicting trends and improving outcomes, this course is ideal. Data Analytics and Social Statistics is designed for any professional working in an industry which uses big data and social data. It is multidisciplinary, using methods that are applicable and relevant in diverse fields, from education, health, and business analytics, to public, private and non-profit sectors such as charities and NGOs.

Whether you have a background in data analytics or are looking for a big data course to gain this knowledge, this course offers a thorough grounding in this exciting field. Incorporating data collection, analysis, and presentation, with acknowledgement of big data and machine learning, this course will ensure you are at the forefront of developments in social data analysis.

This course is suitable for both working professionals who already work in this field and those who wish to change careers. Extensive experience in big data or heavy mathematics skills are not required. If you do not have professional experience in data analytics but have a strong background in social sciences, you can use this course as a conversion to transition into a new vocation in this dynamic field.

What you will learn

Through Data Analytics and Social Statistics, you will learn the practical, applied knowledge to empower you to unleash the true value of data. Throughout this course, you will learn to carry out advanced statistical modelling and create dynamic data visualisations to show new insights. You will create and manage datasets of various sizes, boosting your skills and realising the true potential of these valuable data.

You will also understand the key concepts of uncertainty and randomness in scientific writing. This course will teach you to exhibit a critical awareness of operationalisation and measurement issues in the social sciences. You will gain strong academic writing ability in the social sciences, using independent thinking to express research using data analytics.

Where and when you will study

This course is 100% online, allowing you to study with The University of Manchester from anywhere in the world. You can learn flexibly at a time and pace that suits you. You will gain access to the University’s quality teaching, benefiting from the expertise and reputation of our School of Social Sciences, ranked 5th in the UK (The Times Higher Education Guide 2022).

All course material is available through the virtual learning environment (VLE) and includes video, assessments, workbooks and more. You will also benefit from interactive teaching and the chance to collaborate with your course peers from your global community.

How it will benefit your career

Professionals who can process and interpret rich data are in high demand across many different industries such as public policy, market research, education, non-profit organisations and more. Many of the significant policy challenges of our time are global, from food insecurity, war, disease and public health, and climate change. Big data plays an increasingly important role in helping social scientists understand and address these issues. Analysis of big data has the potential to reveal patterns that are not so easily understood or readily observable, leading to more robust strategies and responses.

Through studying Data Analytics and Social Statistics, you will develop the skills needed to advance in this field and drive your career forward. Learning to leverage data, you will be able to spot and predict trends and understand social behaviour more accurately. If you are looking to change careers, this course will give you a thorough grounding in big data analysis to empower you to make that move.

Course units

  • 1. Data Cleaning and Visualisation Using R (20 credits)

    In this highly practical course unit, you will be introduced to the main building blocks of the R and RStudio software and develop skills in working with R and RStudio in an efficient manner. The unit will cover data management and how to prepare (and tidy) data prior to visualisation and analysis. You will use various R extensions (or ‘packages’) to facilitate different approaches to data exploration, visualisation, and investigation of relationships between variables. Incorporated practical examples will be based on real-world data from across the social sciences.

  • 2. Introduction to Statistical Modelling (20 credits)

    This unit will introduce you to complex quantitative data analysis in the social sciences. It is designed to help you develop technical competence and robust foundations of the underlying principles of the statistical methods employed to interpret analysis output competently. You will use actual data from across the social sciences (e.g., politics, economics, psychology, sociology, criminology, etc.) to build your ability to conduct descriptive, exploratory, and inferential statistics.

  • 3. Data Science Modelling (20 credits)

    This unit aims to prepare you to handle high-dimensional and complex datasets in social sciences (e.g. criminology, politics, sociology, psychology etc.). It is designed to help you develop technical competence and robust foundations in and of the underlying principles of various supervised and unsupervised classification and forecasting methods to interpret analysis output competently. The unit will make use of real data from across the social sciences and will further develop practical skills in R and RStudio software. Ethical considerations will also be integrated throughout the course unit to further cement the integrity-based use of ‘big’ data.

  • 4. Survey Methods and Online Research (20 credits)

    In this unit, we'll introduce you to the principles of survey design for large and small-scale surveys and its contextualised application in academia, public and private sectors. It is intended to help you develop robust theoretical and practical foundations relating to the process of planning, designing, and conducting a survey, and the practical aspects of survey methodology, including ethical considerations. The course will likewise place emphasis on different sampling strategies, survey methodologies, the impact of challenging factors on survey data quality, as well as techniques to address these factors.

  • 5. Multilevel and Longitudinal Analysis (20 credits)

    This unit aims to extend your knowledge to complex survey designs and more intricate data structures in the social sciences. The unit will expand on the concepts, methods and models previously introduced and will further develop programming skills in R and RStudio. The unit will focus on models that can be used to analyse hierarchical data, such as cross-country data or longitudinal data. In this unit, you'll make use of real data originating from surveys of varying complexity to enable you to develop methodologically and statistically robust skills in tackling these complexities in practice.

  • 6. Demographic Forecasting (20 credits)

    Optional unit

    This unit aims to provide you with the skills necessary to derive, interpret, and apply a range of demographic measures to past and present populations at various levels of geography. The unit will develop your ability to critically appraise accuracy and quality of various measures in the light of available data sources. The unit will make use of real data and focus on applying appropriate methods and critically interpreting outcomes such as those of the COVID-19 pandemic in the UK and other countries. Various measures of estimating and forecasting mortality will be emphasised as well other components of population change.

  • 7. Structural Equation Modelling (20 credits)

    Optional unit

    This unit aims to introduce you to the theoretical principles of structural equation and latent variable modelling and provide the required practical skills to run various types of models in R and RStudio. The course unit is designed to help you develop technical competence and robust foundations of the underlying principles of these methods to be able to competently interpret analyses output.

  • 8. Research Skills in Practice (20 credits)

    Mandatory for MSc students

    This course unit will provide you with the opportunity to strengthen your research skills in preparation for the 40-credit dissertation component of the MSc qualification. This course unit is comprised of two topics:

    • Topic 1 will prepare you to develop theory-driven research hypotheses
    • Topic 2 will comprise of approaches to producing an effective and impactful review of the literature in the social sciences.

    The two topics will run in two blocks of 4 weeks and will be assessed independently.

  • Project (40 credits)

    Mandatory for MSc students

    To obtain a Master of Science (MSc), you will need to successfully complete the Research Skills in Practice (RSiP) unit worth 20 credits and deliver a 9,000-word dissertation worth 40 credits.

     

    In your project, you will identify and investigate a research topic of interest relevant to professional practice in the social sciences. The dissertation should take the form of a quantitative research study that utilises secondary social data, preferably from a large-scale survey. Throughout the dissertation period, you will follow a recommended timeline and will receive support through frequent synchronous sessions with your assigned dissertation supervisor.

Course structure

This flexible course is delivered 100% online to allow you to fit your study around your work and other commitments. It explores the fields of data collection, analysis and social statistics using real-world techniques and examples.

Throughout your study, there is ample opportunity for collaboration and networking with your course peers. You will enjoy a high level of support and expertise from your course academics. In this course, you will use the industry standard statistical software - R, allowing you to integrate your learning into your field of work.

This will also empower you to act as a data analysis expert within your workplace, sharing your knowledge to other colleagues for the benefit of the wider team and group projects.

Course learning aims

We've designed this course to create highly competent data analytics professionals who can confidently process data and identify trends across disciplines.

Through studying Data Analytics and Social Statistics, you will reach a high level of competence in data management using real data. You will understand the theoretical underpinnings of statistical methods and gain experience using microdata from different sources.

This course aims to equip you with the ability to critically appraise and carry out social data collection. You will develop a critical awareness of social science data and concepts and use your knowledge to develop original research using data analytics tools.

Through this course, you will be able to confidently present and write about data analytics, improving your skillset and allowing you to cross over to a new industry.

Teaching and learning

This is a flexible, online programme designed to fit around your existing commitments. There are 20 hours of study per week to take when it suits you. We have an extensive array of tools in our virtual learning environment (VLE) including videos, interactive workbooks, self-tests, online tutorials and online assessment.

You will also get to participate in events such as seminars with experts from leading organisations and engagement sessions with your course colleagues. In these sessions, you will have the chance to collaborate and build your network.

Our course academics are world-leading specialists in social science and research, with professional backgrounds analysing data across different disciplines.

Library services

As a student with The University of Manchester, you will be able to use our extensive library services. This will grant you access to books, e-books and journals about social statistics, quantitative data analysis and research, and data science, from introductory to advanced levels.

You will be assigned a dedicated Study Support Advisor who will be your first point of contact for study-related questions and help with the VLE.

Coursework and assessment

All coursework and assessments are completed online involving different methods including individual and group reports, essays, project reports, presentations and quizzes. For assignments that require you to use statistical software, no special licenses are needed as we use R, which is available for free.

If you choose to study on the MSc level, you will also be assessed through the Research Skills in Practice (RSiP) unit and a 9,000-word dissertation. You will get the chance to explore a topic of your choice, contributing to social science and new, innovative interdisciplinary research.

Admissions information

From your initial expression of interest right through to graduation, you’ll receive all the support you need. We will guide you through the enrolment process and help with subject assistance, administrative logistics and fee options, online learning skills, workload management and special circumstances. 

Entry requirements

Academic entry qualification overview

An Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline.

We may also consider exceptional applicants with a Lower Second (2:2) class honours degree if you have research experience or equivalent professional experience.

If you chose to study on a PGDip level and would like to build your qualification to an MSc, you will have to obtain a 50% pass mark on taught units.

English language

If you are not from, or did not graduate from a majority English speaking country , we will also require proof of your English language ability. If you have already taken an English language qualification, please include your certificate with your application.

  • IELTS - overall score of 6.5 with no less than 6.5 in the writing component, or equivalent. Discover more about English language requirements .
English language test validity
Some English Language test results are only valid for two years. Your English Language test report must be valid on the start date of the course.

Application and selection

How to apply

Advice to applicants

To speed up the application process, please submit the following documents with your online application form:

1. Copies of official degree certificates and transcripts of your previous study, showing the subjects taken and grades obtained. If these documents are in languages other than English, please provide official translations in addition to your official certificates and transcripts.

2. English language score report (if applicable) or alternative evidence to demonstrate your English language competency.

3. A copy of your CV detailing your full work experience.

4. Personal statement addressing the questions below (max 500 words)

  • What attracts you to apply to this course?
  • What do you hope to gain from this course and how will it help you achieve your aims?

5. As part of the application process, you will be asked to provide contact details for one referee, professional or academic. The University will contact your referee directly after you submit your application and direct them to complete our online reference form.

Scholarships and bursaries

Postgraduate loans (UK/EU)

If you're an English or EU student living in the UK, you may be eligible for a loan.

Manchester Master's Bursary (UK)

We're committed to helping students access further education.

Manchester Alumni Scholarship Schemes

If you completed your degree at Manchester, you could receive a discount.

Equity and Merit Scholarships

If you're joining us from Uganda, Ethiopia, Rwanda or Tanzania, you can apply for this scholarship.

Global Futures Scholarship

To be eligible, you must be domiciled in one of these countries.

Funding for students with disabilities

If you have a disability, we can help you apply for relevant funding.

Fees and funding

Early application discount (10%) : Apply on or before 22 May 2023 to receive 10% reduction on your tuition fee. To be eligible, you will need to submit a complete application on or before 22 May 2023 and if offered a place, you will need to accept your offer within two weeks from the date of the offer.

Alumni Loyalty Bursary (15%) : If you have successfully graduated from a credit-bearing qualification at The University of Manchester or UMIST, you can receive a 15% discount on the tuition fees that you are personally funding.

One-discount policy : We operate a one discount policy. Early application, Alumni loyalty bursary and scholarships are not accumulative. If you qualify for more than one discount or scholarship, you will be awarded the one that is the highest amount.

September 2023 tuition fees (UK/EU/International):

  • MSc, £15,500
  • PGDip, £10,333

We offer payment by instalments, so you can spread the cost of studying with us.

Employer funding

If you are looking to secure funding from your employer, we can help you build a business case or talk to your employer directly. Contact us on studyonline@manchester.ac.uk to arrange a consultation.

Additional cost information

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Regulated by the Office for Students

The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website.

You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website.