BIT311 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 3, Semester 2
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: BIT101 Programming,BUS107 Database I, BUS303 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. Due to the growth of the internet, there is massive amount of data available to individuals and organisations. Such data cannont be processed or managed using the traditional methods of storing and processing due to the complexity involved. This unit examines the challenges in dealing with large amounts of data faced by organisations 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.

Unit Outline Outcomes (ULO)

  On the successful completion of this units student will be able to:  
ULO1 Discuss the challenges and opportunities of Big Data.  
ULO2 Appraise different big data concepts, tools, techniques, and applications.  
ULO3 Analyse data using available big data tools.  
ULO4 Design appropriate repository structure for storing big data.  
ULO5 Propose big data solutions using Map-reduce techniques.  

Topics to be included  

1. Introduction to big data
2. Non-structured data
3. Organizing, storing, and processing big data
4. Frequent Items
5. Finding similar items    
6. Supervised and unsupervised learning (classification & clustering)
7. Data analytics in big data
8. Data visualization
9. Map-reduce framework
10. Hadoop
11. Big data applications
12. Recommendation Systems & Revision


Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Class Participation 10% 12
Assessment 2: Design Report and Presentation (Group) 30% 8
Assessment 3: Practical Project (Individual) 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.