MIT633 Big Data

Unit outline

Important Update: Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines.  This may include a mix of online and face-to-face.  Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. 
Enrolment modes: Year 2, Semester 2
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: MIT631 Data Analytics
Typical study commitment: Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit.
Scheduled learning activities: 4 timetabled hours per week, 6 personal study hours per week
Other resource requirements: Students will need access to lab computers or will need their own laptops in order to carry out lab exercises and assignments. Students will need to use Apache Hadoop and Apache HBase

Unit description 

This unit covers the key concepts, applications, architectures, and processes that are widely used in big data applications to collect, integrate, analyse and present data, often in different formats and from various sources. It also covers technologies that are commonly used in industry such as NoSQL, Map-Reduce and Hadoop. Students will gain understanding of the challenges faced by organisations for managing large volumes of data.


Unit learning outcomes (ULO)   

 
On the successful completion of this units student will be able to:
ULO1 Evaluate different big data concepts, tools, techniques, and applications.
ULO2 Analyse and visualize data using available big data tools.
ULO3 Design appropriate repository structure for storing big data.
ULO4 Design big data solutions using Map-reduce techniques.
ULO5 Create solutions for data storage and manipulation.

Topics to be included in the unit

1. Introduction to big data
2. Non-structured data
3. Organizing, storing, and processing big data
4. Finding similar items
5. Link Analysis
6. Mining Social Network Graphs
7. Recommendation Systems
8. Data analytics in big data
9. Data visualization
10. Map-reduce framework
11. Hadoop
12. Big data applications & Revision

Assessment

Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Class Participation 10% 12
Assessment 2: Group Report 30% 8
Assessment 3: Individual Project 30% 12
Assessment 4: Final Exam 30% Final exam week

The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.