This module will equip the student with advanced statistical techniques for analysing health related data. It provides the students with the ability to apply appropriate advanced statistical techniques to data sets gathered during project work and to publicly available health related datasets. Students will also evaluate statistical analysis approaches used in existing research studies and comment on their appropriateness.

1. Demonstrate the application of advanced statistical techniques in the analysis of population health data.

2. Describe the selection and application of appropriate quantitative analysis in global health literature and reports.

3. Identify key features of quantitative analysis including descriptive and inferential statistics in a range of study designs

4. Evaluate the appropriateness of statistical approaches in a range of health settings.

5. Perform advanced statistical analysis on health data using SPSS

This module aims to provide a theoretical and practical introduction to Big Data, its analysis and relevant challenges associated with Big Data. Big data, open data and various data infrastructures including the rapidly changing data landscape and data revolution will be discussed. An in depth analysis of the implications of the Big Data and its revolution in various areas (applications of Big Data) will be explored. Technological advancements in storing, accessing, sharing and the cost models will be examined and reviewed. It will also provide an overview of the technical aspects of Big Data analysis along with practical exercises (No prior knowledge of programming would be required as applications for data analysis or scripts can be provided where required).

1. To understand the fundamentals of big data analysis, the strengths and limitations of big data research with real-world examples.

2. To develop competence in handling and processing of Big data, Identify the opportunities and challenges of incorporating big data analytics to improve the development and testing of precision medicine eg. genomic data, electronic medical records.

3. To understand the methodological challenges and problems and gain understanding of the principles of reproducible research (data sharing/version controls etc – e.g GIT)

4. Toapply Health Information Technology and Machine learning techniques to implement relevant workflow for data analysis. Undertake Data Analytics, Data Wrangling and Exploratory Data Analysis (e.g. R, Python)

The aim of this module is to provide students with the core concepts and applications of undertaking economic analysis of healthcare interventions using decision analytic modelling techniques. Decision analytic modelling includes estimating the costs, outcomes and cost-effectiveness of different interventions relative to a status quo or standard of care in healthcare delivery and public health. In particular, these methods are often employed to assess the value of new pharmaceuticals, medical devices and patient management programmes as a basis for health systems to determine whether they should be funded given financial constraints requiring priority setting.

1. To understand the role of decision modelling in economic evaluation to guide decision making

2. To become familiar with the terminology and functionality of basic building blocks of decision analysis

3. To be able to implement the principles of conceptual modelling as a way of planning a model

4. To be able to implement key generic analytic steps in decision analytic modelling such as evidence identification and basic synthesis, sensitivity analysis and reporting results

This module aims to introduce basic concepts, principles, methods and techniques of Data Mining and its applications. It will help develop skills and techniques for practical applications of data mining and engage in the pattern discovery on big data. The importance of pattern discovery and interesting applications of data mining will be discussed. Data mining tasks such as Clustering, Classification, Rule learning and Data mining processes namely Data preparation, task identification and classification/prediction algorithms will be presented. Machine learning algorithms, Neural networks, clustering approaches and text mining applications in Big data will be introduced.

1. To understandbasic concepts, principles, methods and techniques of Data Mining and its applications

2. To develop skills and techniques for practical applications of data mining and engage in pattern discovery on big data (Data Exploration)

3. Toapply techniques inKnowledge Discovery in Databases: From data to knowledge using data mining tools and techniques (Data Mining).

4. To apply techniques in visualization of data to aid data mining and displaying the results of data mining (Data Presentation).