LO1: Handle and manage multiple datasets within R and Python environments.

LO2: Use measures of central tendency to summarize data and assess both the symmetry and variation in the data.

LO3: Present and summarise distributions of data and the relationships between variables graphically.

LO1: Evaluate standard discrete and standard continuous distributions.

LO2: Formulate research hypotheses and perform hypothesis testing.

LO3: Analyse the concept of variance (ANOVA) and an select an appropriate ANOVA or ANCOVA model.

LO1: Carry out global and individual testing of parameters used in defining predictive models.

LO2: Validate assumptions in multiple linear regression.

LO3: Validate models via data partitioning, out of sample testing and cross-validation.

LO1: Develop models using binary logistic regression and assess their performance.

LO2: Develop applications of multinomial logistic regression and ordinal logistic regression.

LO3: Develop generalised linear models and carry out survival analysis and Cox regression.

LO1: Define Principal Component Analysis (PCA) and its derivations and assess their application.

LO2: Understand hierarchical and non-hierarchical cluster analysis and assess their outputs.

LO3: Evaluate the concept of panel data regression and implement panel data methods.

LO1: Appraise classification methods including Naïve Bayes and the support vector machine algorithm.

LO2: Apply decision tree and random forest algorithms to classification and regression problems.

LO3: Analyse Market Baskets and apply neural networks to classification problems.

LO1: Perform text mining on social media data.

LO2: Develop web pages using the SHINY package.

LO3: Apply the Hadoop framework in Big Data Analytics.

LO4: Evaluate the fundamental concepts of artificial intelligence.

LO5: Use SQL programming for data analysis.

LO1: Evaluate the concept of transformation and the key technologies that drive it.

LO2: Assess the strategic impact of the application of Big Data and Artificial Intelligence on business organisations.

LO3: Appraise theories of innovation and distinguish between disruptive and incremental change.

LO4: Evaluate ethics practices within organisations and how they relate to issues in Data Science.