Programme/Approved Electives for 2023/24
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: 1,2Identify methodologies and tools for extracting information from data: 1,2Demonstrate different visualisation techniques in order to present information from data: 1,2Form hypotheses and inferences that relate to real-world problems: 2
Lectures: 22hPracticals: 22hIndependent study: 106h
1: Exercise weighted 50%
Description of Module Assessment
Programming exercisesA set of 3 data science programming exercises spread across the semester. All exercises are provided in a notebook template with answers to be accompanied by appropriate commentary. Students complete the exercises outside of class in open-book conditions and collaboration is forbidden. In total students can expect to spend 4 hours completing the exercises.2: Project weighted 50%
Data analysis project2000-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. . Formatting guidelines will be provided.