Foundations of Data Science

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• State-of-the-art tools to build data-science applications for different types of data, including text and CSV data
• Key concepts in data science, including tools, approaches, and application scenarios
• Topics in statistical analysis and machine learning
• Topics in data collection, sampling, quality assessment and repair
• Topics in data processing at scale

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Understand and apply the fundamental concepts and techniques in data science

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Solve real-world data-science problems and build applications in this space

The course will introduce students to the data scientist toolkit and the underlying core concepts. It will cover the full technical pipeline from data collection (sampling methods, crawling) to processing and basic notions of statistical analysis and visualization. The module will also include advanced topics in high-performance computing, including non-relational databases and MapReduce. By taking this course the students will be provided with the basic toolkit to work with data (CSV, R, MongoDB). To support these learning objectives, the coursework will include exercises and a group project in which students will use existing open data sets and build their own application.

The course will cover the following concepts:

• Fundamentals and core terminology
• Technology pipeline and methods
• Application scenarios and state of the art
• Data collection (sampling, crawling)
• Data analytics (statistical modeling, basic concepts, experiment design, pitfalls, R)
• Data interpretation and use (visualization techniques, pitfalls, D3)
• High-performance computing (parallel databases, MapReduce, Hadoop, NoSQL)
• Cloud computing (principles, architectures, existing technologies)

Lectures and tutorials, as well as coursework (group project, exercises).

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.