Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: CSE313
Module Title: Big Data Analytics
Module Level: Level 3
Module Credits: 5.00
Academic Year: 2017/18
Semester: SEM1
Originating Department: Computer Science and Software Engineering
Pre-requisites: N/A
   
Aims
1. To introduce the environment and the main application domains where Big Data Analytics (BDA) takes place;

2. To introduce general framework and process of BDA;

3. To study technologies and algorithms that support BDA;

4. To study platforms, tools that are currently used in BDA;

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

Learning outcomes 
Upon completing this module, a student will be able to:

A. demonstrate a solid understanding of 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 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. Students will be expected to attend three hours of formal lectures a week, which may run into a “4+2” every fortnight patten in order to arrange lab sessions.

2. Students will be expected to attend up to three two-hour of in-class tutorial to answer issues related to the lectures and lab sessions.

3. Students will be expected to participate in four two-hour supervised practices in a computer lab and expected to spend three times more time continuously for the lab sessions and tasks.

4. Lectures will introduce students to the academic content and practical skills which are the subject of the module, while computer practices will allow students to practise those skills.

5. In addition, the students will be expected to devote roughly two hours of unsupervised time for each lecture hour to solving continuous assessment tasks and private study. Private study will provide time for reflection and consideration of lecture material and background reading.

6. Two assessments will be used to test to what extent the teaching objectives have been achieved. A written examination at the end of the module will assess the academic achievement of the students.

Syllabus 
1. Introduction to Big Data Analytics (6 lectures)

• What is Big data analytics (Advanced analytic techniques operate on big data sets). Differentiate with related concepts: such as: data mining, data analysis, data visualisation, statistics, SQL and data warehouse.

• What is Big data

i) Defining big data via three Vs.

ii) Data sources, data types and the value of the big data

iii) The evolution of the big data and the future of the big data

• What is advanced Data analytics Focuses on inference, the process, the tools of deriving a conclusion based on business environment and organisational goals;

i) Current state of big data analytics

ii) Advanced analytics: exploratory data analysis (EDA) and confirmatory data analysis (CDA);


2. Process and framework (6 lectures)

• Big data pipeline: acquisition, extraction, aggregation, modelling and interpretation.

• Big data source, Hunting for Data,Setting the Goal, Big Data Sources Growing, A Wealth of Public Information

• Realising Value, The Case for Big Data, The Rise of Big Data Options, With Choice Come Decisions


3. Technologies (15 lectures)

Big Data Acquisition, The Storage Dilemma, Bringing Structure to Unstructured Data, Processing Power

Analysis Algorithms, Data Analytics, Big Data and Compliance

Advanced data analysis:

i) Classification

ii) Association analysis: Link analysis and PageRanking

iii) Clustering

iv) Advertisement on Web

v) Recommendation on Web

Security, Compliance, Auditing, and Protection, The Intellectual Property Challenge


4. Systems and software (8 lectures)

• Requirements for a big data analytics solution

• Platforms: Hadoop and Hadoop Distributed File System (HDFS), Map-Reduce, CEP (Complex Event Processing) Streaming Analytics, SQL – related. Extreme SQL No-SQL, Clouds

• Approaches: Choosing among In-house, Outsourced, or Hybrid Approaches


Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 39     6  7    98  150 

Assessment

Sequence Method % of Final Mark
1 Written Examination 80.00
2 Assessment Task 10.00
3 Assessment Task 10.00

Module Catalogue generated from SITS CUT-OFF: 10/22/2017 9:31:50 PM