In today’s fast-paced digital world , the incredible amount of data being generated every minute has grown tremendously from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and GPS signals from cell phone to name a few. This amount of large data with different velocities and varieties is termed as big data and its analytics enables professionals to convert extensive data through statistical and quantitative analysis into powerful insights that can drive efficient decisions. This course provides an in-depth understanding of terminologies and the core concepts behind big data problems, applications, systems and the techniques, that underlie today’s big data computing technologies. It provides an introduction to some of the most common frameworks such as Apache Spark, Hadoop, MapReduce, Large scale data storage technologies such as in-memory key/value storage systems, NoSQL distributed databases, Apache Cassandra, HBase and Big Data Streaming Platforms such as Apache Spark Streaming, Apache Kafka Streams that has made big data analysis easier and more accessible. And while discussing the concepts and techniques, we will also look at various applications of Big Data Analytics using Machine Learning, Deep Learning, Graph Processing and many others. The course is suitable for all UG/PG students and practicing engineers/ scientists from the diverse fields and interested in learning about the novel cutting edge techniques and applications of Big Data Computing.
PREREQUISITES
Data Structure & Algorithms, Computer Architecture, Operating System, Database Management Systems
INDUSTRY SUPPORT
Companies like Amazon, Microsoft, Google, IBM, Facebook
ABOUT THE INSTRUCTOR
Dr. Rajiv Misra is working in Department of Computer Science and Engineering at Indian Institute of Technology Patna, India. He obtained his Ph.D degree from IIT Kharagpur, M.Tech degree in Computer Science and Engineering from the Indian Institute of Technology (IIT) Bombay, and Bachelor’s of engineering degree in Computer Science from MNIT Allahabad. His research interests spanned a design of distributed algorithms for Mobile, Adhoc and Sensor Networks, Cloud Computing and Wireless Networks. He has contributed significantly to these areas and published more than 70 papers in high quality journals and conferences, and 2 book chapters. His h-index is 10 with more than 590 citations. He has authored papers in IEEE Transactions on Mobile Computing, IEEE Transaction on Parallel and Distributed Systems, IEEE Systems Journal, Adhoc Networks, Computer Network, Journal of Parallel and Distributed Computing. He has edited a book titled as “Smart Techniques for a Smarter Planet: Towards Smarter Algorithms” for the “Studies in Fuzziness and Soft Computing” book series, Springer (2018). He has supervised four Phd students and currently four Phd students working under his supervision in the area of big data, cloud computing, distributed computing, and sensor networks. He is a senior member of the IEEE and fellow of IETE. He has completed as the Principal Investigator of R&D Project Sponsored by DeiTY entitled as “Vehicular Sensor and Mesh Networks based Future ITS”. He has mentored the online courses on Cloud Computing, Advanced Graph Theory and Distributed Systems in the platform of NPTEL.
Certification Process
1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.
COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 2000 + GST
2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.
3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:
CRITERIA TO GET A CERTIFICATE
Assignment score = Score more than 50% in at least 6/8 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”
4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.
EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.
5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.
CERTIFICATE TEMPLATE
Course Details
Week 1 : Introduction to Big Data
Week 2 : Introduction to Enabling Technologies for Big Data
Week 3 : Introduction to Big Data Platforms
Week 4 : Introduction to Big Data Storage Platforms for Large Scale Data Storage
Week 5 : Introduction to Big Data Streaming Platforms for Fast Data
Week 6 : Introduction to Big Data Applications (Machine Learning)
Week 7 : Introduction of Big data Machine learning with Spark
Week 8 : Introduction to Big Data Applications (Graph Processing)
BOOKS AND REFERENCES:
TEXTBOOK:
Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014
REFERENCES:
1. Dirk Deroos et al., Hadoop for Dummies, Dreamtech Press, 2014.
2. Chuck Lam, Hadoop in Action, December, 2010.
3. Leskovec, Rajaraman, Ullman, Mining of Massive Datasets, Cambridge University Press.
4. I.H. Witten and E. Frank, Data Mining: Practical Machine learning tools and techniques.
5. Erik Brynjolfsson et al., The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014.
Raguvijaysp –
Good learning