Postgraduate Course: Practical Introduction to Data Science (PGPH11092)

Course Outline
School	School of Physics and Astronomy	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)
Course type	Online Distance Learning	Availability	Not available to visiting students
SCQF Credits	20	ECTS Credits	10
Summary	This course will cover: Why managing data better matters, and why it's hard Data formats: structuring data and keeping them useful Metadata: describing data and keeping them useful Research data management planning Publication and citation of research data Persistence, preservation and provenance of research data Licensing, copyright and access rights: some things researchers need to know Key data analytical techniques such as, classification, optimisation, and unsupervised learning Key parallel patterns, such as Map Reduce, for implementing analytical techniques Practical introductions to key Data Science tools and their application to data science problems, e.g., R, Python *Case studies from academia and business
Course description	Not entered

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information
Not being delivered

Learning Outcomes
On completion of the course you should: Have knowledge of: the common, popular, important data analytics techniques the types of compute and data infrastructures used for data analytics Understand: what data analytics, data science and big data are the importance of data management in general, and in relation to their own potential futures as data professionals the broad global landscape of data management initiatives, infrastructures and projects, and the challenges they address the essential elements in a research data management plan, how to create one, and how to implement it in practice the importance of structuring research data, what standard data formats exist and when and how to use them the importance of descriptive metadata, how to write good metadata (and how to avoid bad metadata), what standard formats exist and when to use them how data are published, cited and preserved, and the issues and challenges we face in recording research data for the long-term some of the legal pitfalls research data creators and users need to avoid Be able to: write programs in R and Python to undertake basic data processing and analysis identify and apply appropriate data analytic techniques to a problem *critically evaluate the analytical performance of a data analytic technique