ADVANCED DATA ANALYTICS

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 Assessment criteria to Type of Summary of

to be met be covered assessment quantity/quality

LO1, LO2 All ACs under LO1, LO2 Coursework 3000 words

LO3 LO3 Lab Demonstration

Indicative Reading list

Evans, J. (2016) Business Analytics. 2nd Ed. Pearson.

Runkler, T. (2016) Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2nd Ed. Vieweg+ Teubner Verlag.

Carlberg, C. (2012) Predictive Analytics: Microsoft Excel. QUE.

Marr, B. (2015) Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance. Wiley.

ADVANCED DATABASE MANAGEMENT SYSTEMS

Unit Reference Number D/617/3036 Unit Title Advanced Database Management Systems Unit Level 6 Number of Credits 20 Total Qualification Time 200 Mandatory / Optional Mandatory SSAs 06.1 ICT practitioners Unit Grading Structure Pass/Fail Number of Credits 20

Unit Aims

The aim of this unit is to develop learners’ knowledge and skills of advanced database systems and how these systems are managed within a business or corporate environment. Learners will be able to make informed choices between vendor and open source platforms for database management systems, and design and develop a relational DBMS for a client using an open source platform.

Learning Outcomes and Assessment Criteria

Learning Outcomes-

Assessment CriteriaThe learner will: The learner can:

1. Understand different types of 1.1 Assess how relational database models and database management normalisation provide reliable and efficient systems. databases. 1.2 Critically compare a range of Database Modelling languages. 1.3 Critically evaluate different database management systems available in relation to open source and vendor-specific platforms. 2. Be able to apply data analysis 2.1 Apply data modelling techniques to refine logical and database design data requirements and normalize. techniques. 2.2 Use a standard unified modelling language (UML) notation to document logical data requirements. 3. Be able to develop a secured 3.1 Design and build a database structure. and functional database to 3.2 Extract data from tables. meet client and system 3.3 Apply table and field-level security to the database. requirements. 3.4 Test the system for functionality and performance 3.5 Evaluate security risks to the database.

Indicative contents

Learning Outcome 1

• Database management systems (DBMS): MySQL, Oracle. • Data models: Entity-Relationship, relational, hierarchical, network, object-oriented, objectrelational. • Relational data structures, including: relations, attributes, domain, tuple, cardinality.