Program Overview
The program aims to develop competence in data management, data modeling, social data collection, and critical data analysis. Graduates will be well-equipped for careers in areas like public policy, market research, education, and nonprofits, where big data and social data analysis are in high demand.
Program Outline
Degree Overview:
The Data Analytics and Social Statistics MSc program is a fully online, part-time course designed to equip students with the skills and knowledge to master social data. It is aimed at professionals working in industries that utilize big data and social data, including education, health, business analytics, public, private, and non-profit sectors. The program's objectives are to:
- Develop competence in data management using real data.
- Understand the theoretical underpinnings of statistical methods.
- Gain experience using microdata from different sources.
- Develop a critical awareness of social science data and concepts.
- Use knowledge to develop original research using data analytics tools.
- Confidently present and write about data analytics.
Outline:
The program is delivered 100% online, allowing students to study flexibly at their own pace. It is structured around a series of modules, each covering a specific aspect of data analytics and social statistics.
Course Units:
- Data Cleaning and Visualisation Using R (20 credits): This module introduces students to the R and RStudio software, covering data management, preparation, and visualization.
- Introduction to Statistical Modelling (20 credits): This module focuses on quantitative data analysis in the social sciences, covering descriptive, exploratory, and inferential statistics.
- Survey Methods and Online Research (20 credits): This module explores the principles of survey design, including ethical considerations, sampling strategies, and data quality.
- Data Science Modelling (20 credits): This module prepares students to handle high-dimensional and complex datasets, covering supervised and unsupervised classification and forecasting methods.
- Structural Equation Modelling (20 credits): (Optional) This module introduces students to the theoretical principles of structural equation and latent variable modelling.
- Research Skills in Practice (20 credits): (Mandatory for MSc students) This module prepares students for the dissertation component of the MSc qualification, covering theory-driven research hypotheses and literature review.
- Project (40 credits): (Mandatory for MSc students) This module involves the completion of a 9,000-word dissertation, where students identify and investigate a research topic of interest relevant to professional practice in the social sciences.
Assessment:
Assessment methods vary depending on the module, but may include:
- Individual and group reports
- Essays
- Project reports
- Presentations
- Quizzes For the MSc level, students are also assessed through the Research Skills in Practice (RSiP) unit and a 9,000-word dissertation.
Teaching:
The program is delivered entirely online, with a variety of teaching methods employed, including:
- Videos
- Interactive workbooks
- Self-tests
- Online tutorials
- Online assessments
- Seminars with experts from leading organizations
- Engagement sessions with course colleagues
Careers:
Graduates of the Data Analytics and Social Statistics MSc program are well-equipped for a variety of careers in fields such as:
- Public policy
- Market research
- Education
- Non-profit organizations The program provides students with the skills to analyze big data and social data, which are in high demand across many industries.
Other:
- The program uses the industry standard statistical software - R, which is available for free.
- Students have access to the University's extensive library services, including books, e-books, and journals on social statistics, quantitative data analysis, research, and data science.
- Students are assigned a dedicated Study Support Advisor who provides support with study-related questions and the virtual learning environment (VLE).
- The academic teaching start date for September 2024 entry is 2 September 2024.
- The welcome event and induction take place one week before the academic teaching start date.
- Students are encouraged to complete their registration ahead of their chosen entry date to gain access to the online learning material and library services.
