CE207-5-SP-CO:
Introduction to Data Science
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 13 January 2025
Friday 21 March 2025
15
16 April 2024
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 data science principles used in data science and their applications, and the use of programming packages for data analysis and visualisation.
Students will also study data analysis techniques, including inferencing, correlation, clustering,
regression, classification.
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.
By the end of this module, students will be expected to be able to:
- Demonstrate a critical understanding of the fundamental concepts of data science principles.
- Demonstrate a procedural understanding of the basics of the Python data science stack.
- Select appropriate Exploratory Data Analysis (EDA) methods and perform EDA
- Demonstrate knowledge of a range of established data analysis techniques
- Apply data analysis techniques to analyse data.
Outline Syllabus:
In the module students will be introduced to the the following data science techniques:
- Python data science stack,
- Distributions,
- Degree of truth,
- Memberships,
- Inferencing,
- Exploratory data analysis (EDA),
- Soft clustering,
- Instance/memory-based classification and regression,
- Dimensionality reduction.
This module will be delivered via:
- A weekly lecture followed by a lab sessions 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
No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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