The module will provide a grounding for students in core concepts in statistics and data analytics, with an emphasis on applications to real-world problems in industry. Topics covered will include an introduction to experimental design, data collection procedures, exploratory data analysis, an introduction to probability, statistical inference, regression analysis and diagnostics. Students will learn how to implement each technique in the statistical programming language R.

1. Data collection procedures: sampling; designed experiments and observational studies; bias and precision in measurement. 2. Exploratory data analysis: frequencies; empirical density curve; percentiles; measures of centre; measures of spread; outliers; barcharts; boxplots; histograms. 3. Introduction to probability: permutations and combinations; axioms, rules of probability; conditional probability; independent events; probability trees; law of total probability; Bayes' rule. 4. Probability distributions: discrete probability distributions (empirical, Binomial, Poisson); continuous probability distributions (the Normal distribution); the normal curve as an idealised histogram; areas under the Normal curve; Normal probability plot; transforming to Normality. 5. Statistical inference: estimation; uncertainty and precision of an estimate; sampling distributions; confidence intervals and hypothesis testing. 6. Cross-classification: cross-tabulation; chi-squared test; Simpson's Paradox. 7. Predictive analytics: correlation; scatterplots; least squares regression line, transforming to linearity; regression diagnostics.

On successful completion of this module, students should be able to: 1. Identify the type and scale of measurement of data and distinguish between them. 2. Summarise data using appropriate graphical and numerical methods. 3. Apply the rules of probability and conditional probability to real-world problems, and understand how to calculate probabilities for discrete and continuous distributions. 4. Construct and interpret confidence intervals, and test hypotheses about population means, proportions and variances, and draw appropriate conclusions. 5. Describe the relationship between two qualitative variables. 6. Describe, quantify the strength of, and model the relationship between two quantitative variables. 7. Implement each technique using the statistical software package R.

On successful completion of this module, students will be able to: 1. Display sharp critical appraisal skills with an appreciation of the appropriate analytics tools to use in a variety of applications. 2. Demonstrate an ability to solve problems, formulate an analysis plan, and justify any decisions made. 3. Synthesise results accurately and effectively and present them.

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This module will be block taught online as part of the UL@Work Human Capital Initiative. This module will create graduates who are KNOWLEDGEABLE (students will develop and apply their analytics knowledge to real-world datasets), COLLABORATIVE (students will work in teams to analyse real-world data), CREATIVE (students will develop skills to build appropriate analytics models), ARTICULATE (students will develop the ability to communicate analytics results to key stakeholders), RESPONSIBLE (students will understand the importance of using appropriate analytics tools and draw suitable conclusions).