Methods of data analytics: Descriptive data analytics, predictive data analytics and prescriptive data analytics. Exploratory data analysis (EDA): Variable identification, univariate and bi-variate analysis, missing values treatment, etc. Learning Outcome 2 Data preparation: Data requirements; data collection, data processing; semi structured/ unstructured metadata processing, cleaning; aggregation; exploratory data analysis (EDA); data product; discretisation, data reduction stages. Data visualisation: Interactive data visualization, Descriptive statistics, Inferential statistics, Statistical graphics, Plot, Data analysis, Infographic Data science Issues: Value leak, compromising trackability of data, forgetting the data prep end users, Data governance Learning Outcome 3 Descriptive analytic techniques • Descriptive statistics: Measures of central tendency, the measure of position and measures of dispersion. • Probability distribution: Cumulate distribution, discrete distribution, continuous distribution. • Sampling and estimation: Random sampling, systematic sampling, point estimate, interval estimate and so forth. • Statistical inferences: Models and assumptions. Predictive analytic techniques • Regression analytics: Linear regression, multiple linear regression and logistic regression. •
Forecasting techniques: Qualitative, average approach, naïve approach, time series methods, causal relationship and so forth.
Prescriptive analytic techniques • Optimisation: Classical optimisation, linear programming techniques, nonlinear programming techniques, dynamic programming. • Decision analysis: Models, justifiable decisions and defensible decisions.
Assessment To achieve a ‘pass’ for this unit, learners must provide evidence to demonstrate that they have fulfilled all the learning outcomes and meet the standards specified by all assessment criteria. Learning Outcomes to be met LO1, LO2 LO3
Assessment criteria to be covered All ACs under LO1, LO2 LO3
London School of International Business| www.LSIB.co.uk
Type of assessment Coursework Lab Demonstration
Summary of quantity/quality 3000 words
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