• Industrial Strategy: Building a Britain fit for the future


    Published at end of 2017 by the UK government.

  • Four Grand Challenges for the industries of the future:


  • Data Economy highlights


  • Summary of the module content

    Aim: allow the students to develop the analytical skills required to analyse and interpret data in an engineering context.
    Theory: the module will use probabilistic, statistical, Fourier and modelling methods to give the students the ability to meaningfully abstract information and make inferences from their data.
    Practical approach: in conjunction with the theoretical aspects of data analysis, this module will also provide the students with the appropriate implementation strategies to perform their data analysis using MATLAB software.


    Lectures, tutorials, labs

    Lectures: Concepts in probability theory, linear algebra, data modelling and data analysis, applications with plenty of solved examples, MATLAB with concepts and solved examples.
         ● Electronic version on GCU Learn

    Tutorials: Problem solving by students & understanding of the theory
         ● Electronic version on GCU Learn

    Labs: MATLAB software – concepts and practical use for computation on vectors and matrices, plotting, linear algebra solutions, random number generation, filtering, smoothing, frequency analysis, regression.
         ● Electronic version on GCU Learn


    Module structure

    Two hours of lectures per week
         ● Concepts and solved problems from probability, linear algebra, data modelling and data analysis
    One hour tutorial each week
         ● Problem solving and understanding of the theory
    Two hours of lab per week
         ● MATLAB software is used for computation, plotting, data modelling, data analysis, data smoothing, Fourier analysis


    Assessment

    Exam (50%)
         ● Mock exam provided near the end of the semester.
    Coursework (25% and 25%)
         ● Mid semester (25%) and end of semester (25%) GCU Learn-based assessment
         ● Multiple choice based assessment of Matlab skills


    Reading

    Counting, Probability and Statistics and concepts
    “Probability and Statistics”; John J. Schiller, R. Alu Srinivasan, Murray R Spiegel, 3rd Edition, 2009 (or 4th edition, 2013)
    Optional MATLAB book:
    “Introduction to MATLAB for Engineers”, Third Edition, William J. Palm III, 2010

  • Theoretical Concepts from Probability - Topics

    Probability, Axioms, Distributions:
    Random experiments, Sample Space, Sample Point, Discrete Events, Set Operations on Events, Probability – Classical and Frequentist Approach, Axioms of probability, Sum and Product Rule, Independence, Joint Probability, Conditional Probability, Bayes Rule, Probability Distributions (Binomial, Uniform, Poisson, Bernoulli), Gaussian distribution and z-score

    MATLAB and Theory Topics

    Linear Algebra: Matrices and vectors, multiplication and addition, transposition, inverse matrices
    Data Analysis: Removing and interpolating missing values, removing outliers, filtering (smoothing and shaping data) – moving average, median filter, de-trending data, robust statistics (trimmed mean, Winsor mean), data trending and visualisation
    Data Modelling: Correlation analysis, correlation coefficient, covariance, cross correlation and autocorrelation, linear regression
    Fourier Analysis: Sampling, Time domain to frequency domain using FFT, magnitude and phase, zero padding, harmonic analysis, power spectrum, spectrum analysis

    Learning Outcomes

    Construct and simulate probabilistic models for various random phenomena
    Successfully select and apply appropriate data smoothing methods to remove noise from data
    Apply correlation and regression methods to explore relationships between time series data
    Understand and utilise frequency domain concepts to extract information from data
    Implement the above methods using a suitable software package such as Matlab

    Teaching and Learning Strategy

    This course focusses predominantly on improving the analytic data analysis skills of the students: appropriate mix of theoretical and practical implementation is required.
    The theoretical material will be introduced through lectures, with the implementation being introduced via practical exercises involving real world contextualised problem solving. Tutorials will be used to explain and elaborate on the lecture material and laboratory exercises.
    To ensure the module is industrially relevant for each programme, real world case studies will be utilised within the laboratories to reinforce the theoretical concepts and expose the students to open ended engineering problems.
    Full use will be made of GCU Learn to provide Lecture-based and related study materials, along with sample solutions of Tutorial and Laboratory exercises, thus encouraging the development of independent learning and allowing self-reflective feedback on student performance. Staff-based feedback on student performance for submitted work will be provided in line with the University feedback policy, with summative feedback and grades on the coursework assessment utilising GCU Learn.

    Teaching approach (short version)


    A Chinese proverb states:

          Tell me, I’ll forget
          Show me, I’ll remember
          Involve me, I’ll understand