Data Science & Computational Methods

Learning Outcomes

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.