This module will help you become proficient in data analysis and computational methods with coding that you will need for solving engineering challenges throughout your degree and beyond.
Aims and Objectives
Learning Outcomes
Syllabus
Introduction To Digital Tools
•Overview of tools for data science: Excel, python, MATLAB
•Cybersecurity
•Data management
•Introduction to AI and Machine learning
Data Processing, visualisation & Insights
•Computational problems/Mathematics in Python
•Statistical Data Analysis
•Linear algebra and systems of equations
•Interpolation, Curve Fitting and Analysis
•Integration
•Root finding and optimisation
Data Handling Methods
•Intro to Python Environment
•Data and workflows
•Variables, data types, operators
•Loops
•Conditional statements, exemptions
•Functions
•Data input/output
•Working with data
•Key libraries
•IDEs and platforms
Applications of data science and computing to discipline specific problems
Learning and Teaching
Teaching and learning methods
A blended learning approach will be used to constructively align the assessment and feedback methods . This will include:
•In-person lectures
•Online content
•Computer laboratories
•Practical workshops