Course aims
The aim of this course is to explain what is meant by data, big data and data science.
The course explains how data has evolved over millennia from simple marks on bones (43000 BCE) into today’s (big) data, discussing the challenges, successes and failures of data, including a particularly (in)famous big data failure by one of the world’s leading data-led firms.
The course also provides a contemporary introduction to data science, explaining its origins from datalogy, computer science and statistics, to its establishment as a stand-alone field and the emergence of the data science profession in the early 2000s. The course then explores the work of data scientists and considers the skills a good data scientist should have.
The course concludes by considering three data science applications: graph networks and how they can can be used to detect financial fraud; how synthetic data is created and its applications; and Natural Language Processing and the challenges involved understanding human language.
Learning Outcomes
1. Understand the core issues of data and big data.
1.1 Explain what is meant by the terms data and big data.
1.2 Explain the Vs of big data.
1.3 Understand the challenges and criticisms of big data.
2. Understand the core issues of data science.
2.1 Understand what is meant by the terms data science and data scientist.
2.2 Understand how data science is related to other academic fields.
2.3 Understand the skills of a good data scientist.
2.4 Understand the challenges and applications of data science.
|
|