Foundations of Data Science

Module overview

Welcome to the Foundations of Data Science! 'Data scientist' has been described as the sexiest job of the 21st century, with the demand for highly skilled practitioners rising quickly to leverage the increasing amount of data available for study. As the amount of data increases, so too does the need for employees who can extract meaningful insights from this data. This course is designed to introduce you to a range of topics and concepts related to the data science process. It will cover the technical pipeline from data collection, to processing, analysis and visualisation. You will be introduced to and gain knowledge of various topics such as statistics, crawling data, data visualisation, advanced databases and cloud computing, along with a toolkit to use with data (including R, D3, Google Refine and Hadoop). The course will include a mix of lectures, tutorials, hands-on exercises and invited talks from expert data science practitioners. Coursework will allow you to gain experience using the theory and techniques delivered in the lectures, while the group project will give you the chance to apply knowledge of the data science process and toolkit in the development of a data science application.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

Topics in data collection, sampling, quality assessment and repair
Topics in statistical analysis and machine learning
Topics in data processing at scale
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

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

Learning and Teaching

Teaching and learning methods

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

Study time
Type	Hours
Completion of assessment task	71
Lecture	12
Wider reading or practice	31
Tutorial	24
Preparation for scheduled sessions	6
Follow-up work	6
Total study time	150

Assessment

Summative

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

Breakdown
Method	Percentage contribution
Continuous Assessment	100%

Referral

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

Breakdown
Method	Percentage contribution
Set Task	100%

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.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External