Course Name: Big Data Computing

Course abstract

In todays 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 todays 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.


Course Instructor

Media Object

Prof. Rajiv Misra

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 Bachelors 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
More info

Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Sep-Nov 2020

  View Course

 Enrollment : 20-May-2020 to 21-Sep-2020

 Exam registration : 14-Sep-2020 to 02-Nov-2020

 Exam Date : 20-Dec-2020

Enrolled

14800

Registered

752

Certificate Eligible

563

Certified Category Count

Gold

1

Silver

92

Elite

336

Successfully completed

134

Participation

66

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Big Data Computing - Toppers list
Top 1 % of Certified Candidates

MD KHALID MASOOD 90%

BANGALORE DATA ENGINEERING CONSULTANTS(BIGDATAEXPERT)

SANJAY SAWHNEY 87%

BSNL

KSHITIJ BANSAL 85%

DR. VISHWANATH KARAD MIT WORLD PEACE UNIVERSITY, PUNE

K SUDHA 84%

MUTHAYAMMAL ENGINEERING COLLEGE

CHANDRAKALA KURUBA 84%

VIGNANS NIRULA INSTITUTE OF TECHNOLOGY & SCIENCE FOR WOMEN

PRASHAKHI DAS 84%

CHIRAG GUPTA 84%

Indian Institute of Technology,Tirupati


Top 2 % of Certified Candidates

V K HANUMAN TURAGA 83%

NATIONAL INSTITUTE OF TECHNOLOGY ANDHRA PRADESH

KIRUTHIVASAN MANIKANDAN 83%

Mu Sigma Business Solutions LLC

MALA H MEHTA 83%

sardar vallabhbhai patel institute of technology

VIJJAPU DILIP 83%

S.R.M. INSTITUTE OF SCIENCE AND TECHNOLOGY

MADHIRA VENKATA SAI ADITYA SHARMA 83%

CMR TECHNICAL CAMPUS

NAGENDRA N 83%

Jawaharlal Nehru Technological University College of Engineering Sulthanpur, Sangareddy District.

K S KOUSHIK 83%

Indian Institute of Technology,Tirupati


Top 5 % of Certified Candidates

JEBA EMILYN J 82%

SONA COLLEGE OF TECHNOLOGY

DR VATSAL SHAH 82%

BIRLA VISHVAKARMA MAHAVIDYALAYA ENGINEERING COLLEGE

NARESH K 82%

NAGARJUNA COLLEGE OF ENGINEERING & TECHNOLOGY

SHREYASH AWASTHI 82%

Zensar Technologies

SANA PARVEEN 82%

ST JOSEPH ENGINEERING COLLEGE

SONALI SURESH SHINDE 81%

COLLEGE OF ENGINEERING PUNE

ALDO STALIN 81%

SONA COLLEGE OF TECHNOLOGY

SACHIN KUMAR 81%

Ameriprise India LLP

SANKARA COURTALLAM RATHINASABAPATHY 81%

UVJ TECHNOLOGIES PVT LTD.

GANESH ROHIT NIROGI 81%

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

PARAM JEET SINGH 81%

Clarivate

G V RAJYA LAKSHMI 81%

LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

SIVASUBRAMANIAN T 81%

TRIYAM

DHARAMVIR SAINI 81%

Government Polytechnic Nanakpur

Enrollment Statistics

Total Enrollment: 14800

Registration Statistics

Total Registration : 752

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.