Practical Introduction to Data Science
Data Science is a rapidly emerging, interdisciplinary field which brings together ideas from computer science, mathematics, statistics, software engineering and beyond. It is concerned with the manipulation, processing and analysis of data to extract knowledge. Data Science is key to making the most of the increasingly large, complex and challenging data sets that are now generated across many areas of science and business.
About the course
This online course will introduce the important ideas and concepts of data science and will allow you to gain the basic skills that would be expected of a data scientist. It has two board themes, namely the importance of looking after data (so that it can be analysed) and data analytics techniques. It's a practical course so you will get to try out these techniques and explore these ideas using common Data Science tools and languages including R and Python.
This course in an assessed course, and on completion of this course you will receive a Postgraduate Professional Development Award of Academic Credit (corresponding to 20 SCQF credits) from the University of Edinburgh.
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
- 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