Module Specifications..
Current Academic Year 2023  2024
Please note that this information is subject to change.
 
Coursework Only A similar set of problems to work on 

Description To introduce the main concepts of data analytics and to give students a working knowledge of the practical application of these techniques in the field of engineering  
Learning Outcomes 1. apply the basic techniques of data analysis 2. apply the fundamental laws of probability 3. demonstrate an awareness of the need for statistical techniques in engineering 4. collate, analyse, present and interpret basic engineering technology data sets 5. gather basic data from codes of practice, databases and other sources  
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml 

Indicative Content and Learning Activities
Introduction Introduction to data analytics, data visualisation and statistical programming languages. The R programming language The basics of the R programming language. Vectors, matrices and data frames. Functions. Working with packages within R. Loading data into R. The shape of data Univariate data. Frequency distributions. Central tendency and spread. Introduction to populations, samples and estimation. Probability distributions. Data visualisation. Describing relationships Multivariate data. Relationships between a categorical and continuous variable. Relationships between two categorical variables. Relationships between two continuous variables. Visualisation methods. Predicting continuous variables Linear models. Simple linear regression. Anscombe's quartet. Multiple regression. Regression with a nonbinary predictor. Probability Definition. Bayes’ Theorem. Random variables. Binomial distribution. Normal distribution. Inferential statistics Estimating means. The Central Limit Theorem. Interval Estimation. The effect of small samples. Introduction to Quality Control Charts using R. Testing hypotheses The null hypothesis significance testing framework. Testing the mean of one sample. Testing two means. Testing more than two means.  
 
Indicative Reading List
 
Other Resources 40555, Website, 0, r, https://stackoverflow.com/, 40556, Website, 0, r, https://www.rproject.org/,  
Programme or List of Programmes  
Date of Last Revision  09JAN06  
Archives: 
