Teaching Responsibility
LJMU Schools involved in Delivery:
LJMU Partner Taught
Learning Methods
Lecture
Module Offerings
4548NCCG-JAN-PAR
4548NCCG-SEP-PAR
4548NCCG-SEP_NS-PAR
Aims
This module will introduce the theoretical foundation of data analytics and a range of data analytic processes and techniques to provide hands-on experience for enhancing students’ skills.
Learning Outcomes
1.
Discuss the theoretical foundation of data analytics that determine decision making processes in management or business environments.
2.
Apply a range of descriptive analytic techniques to convert data into actionable insight using a range of statistical techniques.
3.
Investigate a range of predictive analytic techniques to discover new knowledge for forecasting future events.
4.
Demonstrate prescriptive analytic methods for finding the best course of action for a situation.
Module Content
Outline Syllabus:Data analytics terminologies. Types 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 . Data visualisation: Graphs, charts, plots.
Descriptive statistics: central tendency, position and dispersion. Probability distribution: Cumulate distribution, discrete distribution, continuous distribution. Sampling and estimation. Statistical inferences: Models and assumptions
Regression analytics: Linear regression, multiple linear regression and logistic regression. Forecasting techniques: Qualitative, average approach, naïve approach, time series methods, causal relationships.
Optimisation: Classical optimisation, linear programming techniques, nonlinear programming techniques, dynamic programming.
Decision analysis: Models, justifiable decisions and defensible decisions.