Data Science & Computational Methods

Learning Outcomes

Partial CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

• Select and apply appropriate computational and analytical techniques to model complex problems, recognising the limitations of the techniques employed (assessed via portfolio).
• Communicate effectively on complex engineering matters with technical and non-technical audiences (assessed via portfolio).
• Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems. Some of the knowledge will be at the forefront of the particular subject of study (assessed via portfolio).
• Use a risk management process to identify, evaluate and mitigate risks (the effects of uncertainty) associated with a particular project or activity (assessed via portfolio).
• Evaluate the environmental and societal impact of solutions to complex problems and minimise adverse impacts (assessed via portfolio).
• Adopt a holistic and proportionate approach to the mitigation of security risks (assessed via portfolio).
• Analyse complex problems to reach substantiated conclusions using first principles of mathematics, statistics, natural science and engineering principles (assessed via portfolio).
• Identify and analyse ethical concerns and make reasoned ethical choices informed by professional codes of conduct (assessed via portfolio).

A summary of syllabus content for this module is:

[Data Science]

Visualisation and data handling/ processing

• Plotting graphs and gaining insight from numbers
• Data science methods including data statistics and consideration of uncertainties
• Analysing data including curve fitting, interpolation and function solving
• Advanced Plotting techniques (2D, 3D, time series and time dependent data)
• Tools for data science and data engineering e.g. Excel, Python, Matlab

[Digital Skills – Tools and Platforms]

Introduction to computing tools

• Introduction to Digital Tools e.g. Excel, Python, Matlab and environments such as the Arduino IDE (Integrated Development Environment).

Building Blocks for data analysis (using Python)

• Fundamentals 1: Data and work flows in data science problems
• Fundamentals 2: Variables, data types, objects, loops and branching
• Modular design: prototyping, functions, modules, exceptions and testing
• Data Science fundamentals 1: data/ file input and output
• Data Science fundamentals 2: working with and manipulating data e.g. sequences, lists, arrays, tuples, strings; higher order functions; dictionaries.
• Features of the Python Environment: e.g. Numpy, Scipy, Sympy (symbolic mathematics), Spyder (IDE), Jupyter Notebooks/ JupyterLab
• Advanced techniques: robust software engineering, style guides
• Case Studies/ Exemplars

[Digital Skills - Technologies]

Cybersecurity

• Introduction to cybersecurity (environments, tools, approaches)
• Cybersecurity technologies
• Cybersecurity considerations in data systems design and data handling
• Mitigation of Security risks
• Case Studies/ Exemplars

Wider Topics in digital skills

• Data management
• Machine Learning, Neural Networks, Artificial intelligence, Autonomous systems
• Ethics Concerns in the use of computational technologies
• Responsible use of data science and sources of data
• Societal impact of digital technologies
• Case Studies/ Exemplars

[Computational Methods]

Data Analysis 1: Interpolation, Curve Fitting and Analysis

• What does my data look like? Interpolation and Curve Fitting: Least squares; Polynomials; Splines
• What does my data mean? Analysis and post-processing of simple data – Root finding methods (e.g. derivative-free approaches, Newton-Raphson, Excel Solver).
• Analysis and post-processing of multidimensional data and non-linear systems

Data Analysis 2: Numerical Integration

• How do I calculate quantities from my data? Area under a graph (e.g. Trapezium Rule; Simpson’s Rule; Adaptive quadrature; Advanced techniques)

Linear Systems

• How did we do this analysis? Solution of Linear Systems in

Engineering (e.g. Gaussian Elimination and LU Decomposition)

Numerical Differentiation and Initial Value Problems

• Where does my data come from? Modelling using Finite Differences (Intro); Runge-Kutta Methods (Intro); Experimental data.

Case Studies/ Exemplars

• Application exemplars of techniques in Engineering

This module focuses on a series of practical computational activities, supported by lectures, workshops, self-paced activities and tutorials to introduce supporting skills and theory.

You learn by doing with a particular focus on identifying your own educational needs to direct your own learning. Personal reflection, self-paced and peer-to-peer learning playing an important role.

Summative

This is how we’ll formally assess what you have learned in this module.