Programme/Approved Electives for 2021/22
Available as a Free Standing Elective
Introduction to Data Science I introduces data science, its relationship with business analytics, statistics, machine learning and artificial intelligence. It outlines the key terms and skills required by a data scientist. It provides a practical foundation in commonly used tools for data scientists utilising an interactive learning environment set in real world exercises.
This module introduces data science, its relationship with business analytics, statistics, machine learning and artificial intelligence. It outlines the key terms and skills required by a data scientist and provides a foundation in the tools that are required to apply such skills.
Intended Learning Outcomes
Collect, clean and organise sets of data from the real world.: 1Identify methodologies and tools for extracting information from data.: 1,2Demonstrate different visualisation techniques in order to present information from data.: 1Form hypotheses and inferences that relate to real-world problems.: 2
Lectures: 24hPracticals: 22hExam: 2hIndependent study: 102h
1: Coursework weighted 40%
Description of Module Assessment
Coursework1500-word report on a data science task where students will be given a set of raw data to operate on. Students will be expected to extract the right data from the given set (thus demonstrating what they would choose to collect in the first place), use appropriate methods to clean and organise the data and then apply basic data mining methodologies in order to extract information that would aid the solution of the given problem. Finally the students will choose appropriate visualisation techniques to present the information extracted.2: Exam weighted 60%
2-hour unseen examination2-hour unseen examination. Students will pick 2 questions from 3.