With the advance of IT storage, pcoressing, computation, and sensing technologies, Big Data has become a novel norm of life. Only until recently, computers are able to capture and analysis all sorts of large-scale data from all kinds of fields -- people, behavior, information, devices, sensors, biological signals, finance, vehicles, astronology, neurology, etc. Almost all industries are bracing into the challenge of Big Data and want to dig out valuable information to get insight to solve their challenges.
This course shall provide the fundamental knowledge to equip students being able to handle those challenges. This discipline inherently invoves many fields. Because of its importance and broad impact, new software and hardware tools and algorithms are quickly emerging. A data scientist needs to keep up with this ever changing trends to be able to create a state-of-the-art solution for real-world challenges.
This Big Data Analytics course shall first introduce the overview applications, market trend, and the things to learn. Then, I will introduce the fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data. Afterwards, the course will introduce several data storage methods and how to upload, distribute, and process them. This shall include HDFS, HBase, KV stores, document database, and graph database. The course will go on to introduce different ways of handling analytics algorithms on different platforms. Then, I will introduce visualization issues and mobile issues on Big Data Analytics. Students will then have fundamental knowledge on Big Data Analytics to handle various real-world challenges.
Afterwards, the course will zoom in to discuss large-scale machine learning methods that are foundations for artificial intelligence and cognitive networks. The course will discuss several methods to optimize the analytics based on different hardware platforms, such as Intel & Power chips, GPU, FPGA, etc. The lectures will conclude with introduction of the future challenges of Big Data, especially on the onging Linked Big Data issues which involves graphs, graphical models, spatio-temporal analysis, cognitive analytics, etc.
Students will choose the topics of their own for a final project. The application domain can be based on the students' own interest. This will be a good opportunity for students to apply what's learned in the class for their needs, either for the future work requirements or for the research problems at hand.
Prerequisite(s)
Corequisite(s)
Special Requisite(s)
Instructor(s)
Course Assistant(s)
Schedule
Office Hour(s)
Teaching Methods and Techniques
Principle Sources
Other Sources
Course Schedules
Week
Contents
Learning Methods
1. Week
Overview of big data analytics
Oral presentation, Laboratory, Project
2. Week
Big data analytics platforms
Oral presentation, Laboratory, Project
3. Week
Big data storage and analytics
Oral presentation, Laboratory, Project
4. Week
Clustering and classification
Oral presentation, Laboratory, Project
5. Week
Big data analytics Algorithms-II
Oral presentation, Laboratory, Project
6. Week
Spark and Data analytics
Oral presentation, Laboratory, Project
7. Week
Linked big data - Graph computing
Oral presentation, Laboratory, Project
8. Week
Midterm
Written exam
9. Week
Linked big data - Graph analytics
Oral presentation, Laboratory, Project
10. Week
Graphical models
Oral presentation, Laboratory, Project
11. Week
Big data visualisation
Oral presentation, Laboratory, Project
12. Week
Cognitive mobile analysis
Oral presentation, Laboratory, Project
13. Week
Applications
Oral presentation, Laboratory, Project
14. Week
Project presentations
Oral presentation, Laboratory, Project
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Homework / Term Projects / Presentations
1
20
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Competence on the topics of Big Data storage, analytics, supervised and unsupervised learning from Big Data.
LO-2
Having sufficient knowledge to be able to realize various applications on Linked Big Data and, graphical representation and analytics of it.
LO-3
To be able to recognize current application fields and to be able to propose projects in those related fields.
