Big Data Management detail syllabus for Biotechnology (BT), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17BT754), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.
For all other bt 7th sem syllabus for be 2017 scheme vtu you can visit BT 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-4 subjects do refer to Professional Elective-4. The detail syllabus for big data management is as follows.
Course Objectives:
This course will enable students to
- Learn what big data is, and how it differs from traditional approaches
- Learn about the Plan and use the primary tools associated with big data in creating systems to take advantage of big data.
- Extract knowledge and intelligence from datasets which exhibit high volume, velocity, and/or variety.
- Plan and execute a project that includes the use of at least one big data dataset.
- Discuss the meta issues around big data such as governance, security, privacy, and OAM&P.
- Execute analyses oriented to streaming data.
- Have a framework with which to understand new advances in the field, and distinguish hype from reality.
- Discuss organizational issues related to big data.
Module 1
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Module 2
DATA ANALYTICS IN A BIG DATA: Data analytics in a big data, distributed world. R over Hadoop. Issues related to the governance of large data sets, including: security, privacy, integrity, quality, and OA&M. More detailed discussion of the issues of security, privacy, integrity, quality, OA&M, and management of big data, including related technologies. Privacy Policies of selected companies. Discussions of selected applications of big data in a few different industries.
Module 3
DATABASE MANAGEMENT: Parallel database management, Distributed databases and distributed query processing, MapReduce and other parallel programming models, Big data: theory and practice, Volume: tractability revisited; parallel scalability; bounded evaluability, techniques for querying big data, by making big data small, Veracity: data quality, the other side of big data; central issues of data quality; dependencies for improving data quality; discovering data quality rules; cleaning distributed data; data repairing; entity resolution.
Module 4
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Module 5
DATA ANALYTICS:
Data analytics using clustering, algorithms and frameworks; Categories of clustering algorithms; Data analytics using supervised learning and classification; Multi-class classification; Differences and shared challenges between classification and clustering; Classification based models for clustering; Spatio-temporal data structures for range queries for data mining applications; Intricacies of image feature extraction for content-Based image retrieval.
Course Outcomes:
After studying this course, students will be able to:
- Understand and discuss what big data is, and how it differs from traditional approaches
- Plan and use the primary tools associated with big data in creating systems to take advantage of big data.
- Extract knowledge and intelligence from datasets which exhibit high volume, velocity, and/or variety.
- Plan and execute a project that includes the use of at least one big data dataset.
- Understand and discuss the meta issues around big data such as governance, security, privacy, and OAM&P.
- Understand and be able to execute analyses oriented to streaming data.
- Have a framework with which to understand new advances in the field, and distinguish hype from reality.
- Understand and discuss organizational issues related to big data.
Reference Books:
- Big Data Management and Processing, Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, CRC Press, 2017
- Large Scale and Big Data: Processing and Management, SherifSakr, Mohamed Gaber, CRC Press, 2016
Text Books:
- Big Data Management, Editors: Garcia Marquez, Fausto Pedro, Lev, Benjamin (Eds.) 2017
- Big Data Management, Technologies, and Applications by Naima Kaabouch, Wen-Chen Hu, Publisher: IGI Global, October 2013.
For detail syllabus of all other subjects of BE Bt, 2017 regulation do visit Biotech 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.