Data Management, Data Analysis and Statistics
It is increasingly important that researchers consider how data is acquired, analysed, presented and stored. Such an understanding not only ensures that valid and worthy science is performed and creates impact, but is also a requirement of publishers and funding agencies. This course will provide an introduction to best practice in scientific data management and curation, and to systems to help students curate, manage and publish experiments. We will discuss the challenges involved in data security, data curation and data sharing and strategies to address them. The importance of the full consideration of statistics in the planning, execution and reporting of science will also be explained. Group exercises will provide experience of processing experimental data to produce ‘publication ready’ figures and text. Topics to be covered include:
- Best practice in making and keeping records of experimental and computational research
- An introduction to data sharing
- The role of the publishing industry and funding bodies in shaping the future of scientific data management
- Tools and systems for data curation and publication
- Emerging trends and technologies in academic and industrial data science
- Fundamental statistics for the planning, execution and reporting of scientific research including probability, hypothesis testing, ANOVA, regression analysis.