|Course title||Big Data Analytics
|Course description||This course aims at teaching students the state-of-the-art big data analytics, including techniques, software, applications, and perspectives with massive data. The class will cover, but not be limited to, the following topics: advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, CloudStore, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; managing advertising and recommendation systems for Web applications. The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services, or other scientific applications.
|Exclusion||CMSC5741 or CSCI5510|
|Semester||1 or 2|
|Grade Descriptors||A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
|Learning outcomes||At the end of the course of studies, students will have acquired the ability to
1. understand the key issues on big data and the associated applications in intelligent business and scientific computing.
2. acquire fundamental enabling techniques and scalable algorithms in big data analytics.
3. interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
4. achieve adequate perspectives of big data analytics in marketing, financial services, health services, social networking, astrophysics exploration, and environmental sensor applications, etc.
(for reference only)
|Essay test or exam: 40%
Lab reports: 30%
|Recommended Reading List||1. Frank J. Ohlhorst. Big Data Analytics: Turning Big Data into Big Money. Wiley. 2012.
2. Toby Segar. Programming Collective Intelligence: Building Smart Web 2.0 Applications. O’Reilly Media, 2007.
3. Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media, 2011.
4. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2011.
|CSCIN programme learning outcomes||Course mapping|
|Upon completion of their studies, students will be able to:|
|1. identify, formulate, and solve computer science problems (K/S);||T|
|2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
|3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);||T|
|4. communicate effectively (S/V);
|5. succeed in research or industry related to computer science (K/S/V);
|6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);||TP|
|7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);|
|8. practise high standard of professional ethics (V);||P|
|9. draw on and integrate knowledge from many related areas (K/S/V);
|Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured|