View Specification DocumentData Science and Application (UESTC) UESTCHN5006
- Academic Session: 2026-27
- School: School of Engineering
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
Data science and applications mainly include data analysis and mining, as well as popular topics such as machine learning and deep learning. Data analysis is the process of cleaning, preprocessing, conducting detailed research, and summarizing various data using appropriate statistical methods, with a greater emphasis on understanding and organizing the business flow of data; Data mining focuses more on how to identify valuable hidden events in large amounts of data, and predict, analyze, and learn from them. This course mainly focuses on the integration technology of data mining, introducing techniques such as classification, prediction, association, and clustering.
Timetable
This course will be timetabled in blocks, typically one week in four.
Requirements of Entry
None
Excluded Courses
None
Co-requisites
None
Assessment
30% Coursework (including homework)
20% Course design and project (including report and the oral presentation of the project)
50% Final exam (closed-book exam)
Main Assessment In: December
Course Aims
Through the study of this course, students will not only master the basic methods of business understanding, data collection, data preprocessing and data presentation in data analysis, but also the commonly used methods of prediction model (regression, logic, time series), clustering algorithm, association rule discovery and classification, deviation detection and so on in data mining.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Master the basic skills, methods and ideas of data analysis and mining.
2. Analyse and solve problems when facing industry data.