Program Overview

Data Analytics and Social Statistics

Overview

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 science data effectively, improving your skills and professional outcomes in the process.

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

Key Features

• Applied and practical learning
• Research and teaching excellence
• Latest methods and techniques
• Interdisciplinary approach covering latest methods such as machine learning

Key Information

• Delivery: 100% online learning
• Qualification: MSc (180 credits) or PGDip (120 credits)
• Duration: MSc - 27 months, part-time; PGDip - 18 months, part-time
• Application Deadline: 18 August 2025
• Induction: One week before teaching begins
• Academic Teaching Start Date: 1 September 2025
• Workload: Approx 20 hours per week
• Course Director: Dr Alexandru Cernat, Senior Lecturer in the Department of Social Statistics

Fees and Funding

• Total Course Tuition Fees: MSc - £16,500 (UK/EU/International); PGDip - £11,000 (UK/EU/International)
• Payment by Instalments: Available
• Scholarships and Bursaries: Manchester Master's Bursary (UK), Manchester Alumni Scholarship Schemes, Equity and Merit Scholarships

Entry Requirements

• Academic Entry Qualification: Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline
• English Language: IELTS - overall score of 6.5 with no less than 6.5 in the writing component, or equivalent

Course Units

• 1. Data Cleaning and Visualisation Using R (20 credits)
  • Knowledge and understanding
    • State and define the basic concepts underpinning statistical programming
    • Organise complex data management tasks in R and RStudio
    • Outline the principles of the ‘grammar of graphics’
    • Identify and explain the value and limitations of statistical programming
    • Recognise the value of reproducible data practices
    • Critically assess data workflows
  • Intellectual skills
    • Devise advanced data management plans using statistical programming
    • Produce well-reasoned arguments for the suitability of various management and visualisation approaches given different data types
    • Evaluate ‘messy’ data and develop well-justified cleaning procedures
  • Practical skills
    • Execute data recoding and reshaping tasks in R and RStudio to extract new insights
    • Implement appropriate methods to quantify and assess relationships among variables in R and RStudio
    • Create reproducible workflows with different types of data
  • Transferable skills and personal qualities
    • Manage, recode, and reshape data in R and RStudio
    • Summarise, visualise, and analyse data in R and RStudio
    • Create ‘clean’, consistent, and well-organised R code
    • Produce text output in various formats using RStudio
    • Formulate, organise, express, and communicate data-driven opinions effectively
    • Communicate statistical information using suitable and effective graphics
• 2. Introduction to Statistical Modelling (20 credits)
  • Knowledge and understanding
    • Describe the concept of statistical inference and the relationship between population, samples, and uncertainty
    • Outline the assumptions of different statistical methods and recognise their practical value and limitations
    • Discuss the challenges associated with causal inference in the social sciences
    • Recognise the importance of conceptual and theory-driven research in the social sciences
    • Formulate mathematical functions to represent statistical models
  • Intellectual skills
    • Interpret and assess statistical output from a variety of methods
    • Synthesize and critically evaluate academic literature
    • Derive evidence-based arguments for/against competing hypotheses
    • Develop a causal reasoning approach to hypothesis-driven research
  • Practical skills
    • Describe, summarise, and visualise different data types using R and RStudio
    • Utilise appropriate methods to quantify, model, and assess relationships between variables in R
    • Interpret results of a variety of exploratory and inferential statistical methods
  • Transferable skills and personal qualities
    • Critically assess quantitative research
    • Utilise statistical programming software (R and RStudio) to interrogate data and draw valuable insights
    • Formulate, organise, express, and communicate data-driven opinions effectively
    • Communicate statistical information using suitable and effective graphics and statistical models
• 3. Survey Methods and Online Research (20 credits)
  • Knowledge and understanding
    • Recognise the importance of conceptual and theory-driven survey designs
    • Summarise ethical considerations associated with survey design and implementation
    • Define key terminology utilised in survey design
    • Outline factors which influence data quality
    • Differentiate between sampling techniques and methods
    • Discuss the importance of measurement error and estimation
    • Describe the interconnected relationship amongst the different steps and components of survey research in the social sciences
  • Intellectual skills
    • Assess suitability of and make evidence-based judgements about sampling techniques in the context of different research questions
    • Evaluate survey questionnaires in the context of different survey designs
    • Ability to critically assess the quality of surveys and survey data
    • Specify well-reasoned arguments for selecting post-survey processing and estimation methods
  • Practical skills
    • Design and plan a survey (online and in the field)
    • Create questionnaires to collect suitable information for specific research questions, taking into considerations limiting factors for different types of designs
  • Transferable skills and personal qualities
    • Prepare a survey proposal
    • Formulate, organise, express, and communicate evidence-based opinions effectively
• 4. Data Science Modelling (20 credits)
  • Knowledge and understanding
    • Summarise the necessary procedures for handling high dimensional and complex datasets
    • Outline the principles underlying the methods and models addressing different classification and forecasting problems
    • Discuss the ethical implications of machine learning and ‘big data’
  • Intellectual skills
    • Ability to select the appropriate analytical tools given specific applications
    • Interpret statistical output from a variety of methods and models
    • Assess statistical techniques in the context of research question and data used
  • Practical skills
    • Devise justified plans to handle large and complex datasets using R and RStudio
    • Select and estimate supervised and unsupervised learning models in the context of empirical applications
  • Transferable skills and personal qualities
    • Utilise statistical programming software (R and RStudio) to interrogate high-dimensional data and draw valuable insights using various machine learning techniques and models
    • Formulate, organise, express, and communicate data-driven opinions effectively
• 5. Multilevel and Longitudinal Analysis (20 credits)
  • Knowledge and understanding
    • Recognise different types of nested data in the social sciences and how they impact modelling decisions
    • Specify how longitudinal data can be used to answer key questions in the social sciences
    • Distinguish between different methods for addressing nested and longitudinal data structures
  • Intellectual skills
    • Interpret statistical output from a variety of advanced quantitative methods
    • Appraise academic literature that uses nested and longitudinal data
    • Select appropriate statistical methods to account for different nested and longitudinal data structures
    • Defend methodological selection(s) using justified, evidence-based arguments
  • Practical skills
    • Develop an appropriate design, plausible model, and appropriate method of analysis of nested and longitudinal data
    • Utilise statistical computing software (R and RStudio) to implement various complex methods (e.g. multilevel model for change)
    • Identify and implement suitable methods for interrogating complex data
    • Interpret results of a variety of methods suitable for nested and longitudinal data
    • Assess the quality, practical value, and limitations of estimated statistical models
  • Transferable skills and personal qualities
    • Apply suitable statistical methods and models to gain valuable insights from complex data using R and RStudio
    • Formulate, organise, express, and communicate data-driven opinions effectively
    • Communicate statistical information using suitable and effective graphics
• 6. Demographic Forecasting (20 credits)
  • Knowledge and understanding
    • Describe key concepts and theories related to population change and population components
    • Discuss the measures used to analyse population change
    • Recognise suitable methods and data commonly used to measure and forecast population change
  • Intellectual skills
    • Select, summarise, and evaluate information from various sources as well as academic literature on demographic forecasting
    • Critique methods used in measuring and forecasting population change
    • Compose justified arguments for/against different statistical methodologies and take appropriate decisions regarding measuring population change in different contexts
  • Practical skills
    • Produce a range of demographic measures using statistical techniques in R and RStudio software
    • Assess quality of claims by media and statistical authorities about population change
  • Transferable skills and personal skills
    • Utilise statistical computing software (R and RStudio) to interrogate data and draw valuable insights about population structure and population change
    • Apply appropriate methods on real world data in various contexts
    • Formulate, organise, express, and communicate data-driven opinions effectively
• 7. Structural Equation Modelling (20 credits)
  • Knowledge and understanding
    • Recognise the nature of structural equation modelling and its relationship to other statistical methods
    • Distinguish between types of models used given different variables of interest
    • Identify the contexts where different structural equation models are appropriate
  • Intellectual skills
    • Evaluate latent variable and/or structural equation modelling published in scholarly journals
    • Translate conceptual theories/hypotheses into appropriate latent variable and structural equation models
    • Derive appropriate scientific inferences from the results of structural equation models
  • Practical skills
    • Utilise R and RStudio to specify and fit a range of structural equation and latent variable models to social datasets
    • Interpret the parameter estimates generated by different structural equation and latent variable models
  • Transferable skills and personal qualities
    • Synthesise evidence from relevant literature and individual analyses
    • Formulate research questions and apply appropriate statistical models to address them
    • Formulate, organise, express, and communicate data-driven opinions effectively
• 8. Research Skills in Practice (20 credits)
  • Knowledge and understanding
    • Demonstrate in-depth theoretical and practical knowledge and critical awareness of current topics of interest and debate in data analytics and social statistics
    • Recognise the importance of a robust research framework in drawing valid, data-driven conclusions
  • Intellectual skills
    • Identify and evaluate various statistical methods for addressing different research topics
    • Apply critical appraisal skills to real world situations pertinent to the social sciences
  • Practical skills
    • Find, evaluate, and synthesise information from scholarly journals and other sources of information
    • Develop theory-driven research questions in the social science context
  • Transferable skills and personal qualities
    • Develop a research proposal
    • Conduct critique of academic literature and evaluate various information sources
    • Formulate, organise, express, and communicate evidence-based opinions effectively
• Project (40 credits)
  • Knowledge and understanding
    • Demonstrate broad and in-depth knowledge of current trends and debates in data analytics and social statistics
    • Show critical engagement with research and scholarship
    • Recommend improvements in social science professional practice and research agenda, including their own study
  • Intellectual skills
    • Evaluate relevant bodies of literature in the social sciences
    • Appraise different theoretical perspectives in the social sciences and make evidence-based assessments
    • Identify an appropriate range of research methods for a chosen dataset
    • Develop robust research questions or hypotheses
    • Synthesise and evaluate findings
    • Deliver clear evidence-based and data-driven conclusions and recommendations
    • Exhibit critical reasoning through generating research findings that add to the existing body of knowledge in the social sciences
  • Practical skills
    • Conduct literature reviews using a range of bibliographic techniques and sources
    • Analyse, synthesise, and apply relevant concepts and methods to a chosen topic
    • Design and conduct a research project using secondary data
    • Write using appropriate format, structure, presentation, language, and tone suitable for academic audiences
  • Transferable skills and personal qualities
    • Analyse and evaluate academic literature and other sources of information and evidence
    • Apply problem-solving skills to identify and address research gaps using suitable methodological approaches
    • Assess potential shortcomings in the research process and derive appropriate solutions to address these in a timely manner
    • Illustrate high-quality writing using language appropriate to an academic audience
    • Recognise, evaluate, and draw appropriate conclusions given methodological limitations
    • Utilise Information Technology effectively to search databases and other internet resources for literature and produce documents using word processing and presentation programs

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.

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: Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline
• English Language: IELTS - overall score of 6.5 with no less than 6.5 in the writing component, or equivalent

Scholarships and Bursaries

• Manchester Master's Bursary (UK): Available
• Manchester Alumni Scholarship Schemes: Available
• Equity and Merit Scholarships: Available

Fees and Funding

• Total Course Tuition Fees: MSc - £16,500 (UK/EU/International); PGDip - £11,000 (UK/EU/International)
• Payment by Instalments: Available
• Postgraduate Loans (UK/EU): Available
• Funding for Students with Disabilities: Available

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).