CE207-5-SP-CO:
Introduction to Data Science
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 15 January 2024
Friday 22 March 2024
15
29 November 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BSC I400 Artificial Intelligence,
BSC I401 Artificial Intelligence (Including Foundation Year),
BSC I402 Artificial Intelligence (including Placement Year),
BSC I403 Artificial Intelligence (including Year Abroad)
This module is designed to provide students with an introduction to the statistical principles used in data science and their applications, and the use of practical programming packages for data analysis and visualisation.
Students will also study data analysis techniques, including causal inference, correlation, classification, regression, and clustering.
The aim of this module is to familiarise students with the main concepts, techniques and challenges involved in data science applications and to provide students with practical programming experience.
After completing this module, students will be expected to be able to:
1. Demonstrate a critical understanding of the fundamental concepts of statistical principles
2. Demonstrate a critical understanding of the basics of the Python data science stack
3. Visualise and present models and data interpretations formally
4. Demonstrate knowledge of a range of established data analysis techniques
5. Apply data analysis techniques to analyse complex data
Outline Syllabus
1. Introduction
2. Probability
Probability
Probability distribution
Mean, variance, correlation
3. Statistic inference
Estimation
Causal inference
4. Python data science stack
NumPy
Pandas
5. Data visualisation
Examining variables and relationships in graph
Interactive graphs
High dimensional data visualisation
6. From data and observations to models
Modelling data
Classification with K-nearest neighbour and linear discriminant analysis
Linear regression
7. Data exploration
Clustering
Feature selection
Principle component analysis
Data transformation
Every lecture will be followed by a lab session where the ideas will be put into practice.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Progress Test |
|
25% |
| Coursework |
Coursework Assignment, Introduction to Data Science coursework |
|
75% |
| Exam |
Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period)
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Javier Andreu-Perez, email: j.andreu-perez@essex.ac.uk.
Dr Andreu-Perez
csee-schooloffice@essex.ac.uk
No
No
Yes
Dr Adam Chester
University Of Warwick
Associate Professor
Available via Moodle
Of 24 hours, 0 (0%) hours available to students:
0 hours not recorded due to service coverage or fault;
24 hours not recorded due to opt-out by lecturer(s), module, or event type.
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