Aims and Fit of Module

•	To introduce the environment and the main application domains where Big Data Analytics (BDA) takes place;

•	To introduce general framework and process of BDA;

•	To study technologies, and platforms and , tools that are currently used in BDA;

•	To study methods and algorithms that support BDA;

•	To gain an understanding of the best practice in BDA. 

Learning outcomes

A.	Demonstrate a solid understanding of concepts, processes and issues related to Big Data Analytics (BDA);


B. Identify applications of BDA that can help improve business operations;

C.	Determine the appropriate use of analysis methods, algorithms, technologies, tools, and software packages to support data analysis involving practical scenarios; 

D.	Be proficient with at least one data analytics software package.

Method of teaching and learning

1.	Introduction to Big Data Analytics (4 lectures)

A.	Data and data types;

B.	Big data and Types

C.	Big data analytics


2.	BDA Process and Tasks (2 lectures)

3.	BDA platforms and tools (4 lectures + 8 tutorials)

A.	Hadoop and MapReduce

B.	Spark 

C.	NoSQL and MongoDB


4.	BDA methods and algorithms (18 lectures)

A.	Data preparation

B.	Descriptive Data analyses 

C.	Explorative data analyses

D.	Predictive data analyses

E.	Prescriptive Data analyses

5.	Best business practice (4 lectures)

Start Small with Big Data, Thinking Big, Avoiding Worst Practices, Hands-on Big Data, Big Data Visualisation, ownership and security