MSc Data Analytics and Social Statistics

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 the 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.

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 with other colleagues for the benefit of the wider team and group projects.

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.

Data Cleaning and Visualisation Using R

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.

Introduction to Statistical Modelling

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.

Survey Methods and Online Research

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.

Data Science Modelling

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.

Multilevel and Longitudinal Analysis

This unit aims to extend your knowledge of 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.

Demographic Forecasting

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 as other components of population change.

Structural Equation Modelling

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 analysis output.

Research Skills in Practice

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

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.

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.

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. By 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.

Delivery - 100% online learning

Duration:

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

Academic teaching start date - 1 September 2025

Application deadline - 18 August 2025

Workload - Approximately 20 hours per week

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.

The welcome event and induction take place one week before the academic teaching start date. Our admissions team will confirm your induction date closer to the time.

Please ensure that you complete your registration ahead of your chosen entry date to gain access to the online learning material and library services.