Data Acquisition and Processing Systems (DPS) (ELEC0136)

Key information

Faculty: Faculty of Engineering Sciences
Teaching department: Electronic and Electrical Engineering
Credit value: 15

Restrictions: Only available to TMSIMLSSYS01, UMNEENSEEE14, UMNEENSINT14, UMNEENWCME14, UMNEENWCOM14, CPD and 果冻影院 Short Courses. Applicants for this module should possess a good understanding of foundational mathematical concepts in linear algebra, probability, and statistics (e.g., matrix multiplication, descriptive statistics, the distinction between samples and populations, etc.), as well as fundamental principles in mathematical analysis and signal processing (e.g., correlation, Fourier Transform, convolutions, etc.). Additionally, applicants should be familiar with at least one programming language, preferably Python.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module will cover technology, principles and applications of data collection, preparation, and

storage for data science systems. In particular, the module will cover a wide range of topics such as

sampling theory and practice, data collection, database query and processing, data processing, and feature engineering techniques. The module will also encompass practical assignments (in Python) so that students can learn how to apply the underlying principles to address problems in the areas of database queries and processing, data wrangling, and processing.

Syllabus:

This module will cover the following topics:

鈥� Introduction to Data & Data Acquisition

鈥� Data Preparation

鈥� Data Storage

鈥� Feature Engineering

鈥� Hardware & Methods for Efficient Data Processing

鈥� Data Representation

鈥� Data Modelling, Analysis and Processing

听

Learning Outcomes:

At the end of this module, participants should be able to

鈥� Understand the concepts, techniques & tools for data acquisition, processing & analysis鈥攂oth in

theory and practice; (e.g., data collection/ sampling/ processing/ integration/ feature engineering/

exploration, efficient data processing, ethical considerations, ML techniques, etc.)

鈥� Understand the hardware architectures associated with data processing and machine learning;

鈥� Understand & implement tools for handling large-scale datasets in real-world applications;

鈥� Acquire hands-on experience with popular ML libraries, well-known real datasets, databases, and

Python.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Coursework
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 35
Module leader: Dr Laura Toni
Who to contact for more information: eee-msc-admin@ucl.ac.uk

Intended teaching term: Term 1 听听听 Undergraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Coursework
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 30
Module leader: Dr Laura Toni
Who to contact for more information: eee-msc-admin@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

听

果冻影院