Big Data Computing

COURSE OUTLINE : 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 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.

noc19-cs33-Introduction-Big Data Computing.
noc19-cs33 Lecture 1-Introduction to Big Data.
noc19-cs33 Lecture 2-Big Data Enabling Technologies.
noc19-cs33 Lecture 3-Hadoop Stack For Big Data.
noc19-cs33 Lec 04-Hadoop Distributed File System (HDFS).
noc19-cs33 Lec 05-Hadoop MapReduce 1.0.
noc19-cs33 Lec 06-Hadoop MapReduce 2.0 (Part-I).
noc19-cs33 Lec 07-Hadoop MapReduce 2.0 (Part-II).
noc19-cs33 Lec 08-MapReduce Examples.
noc19-cs33 Lec 09-Parallel Programming with Spark.
noc19-cs33 Lec 10-Introduction to Spark.
noc19-cs33 Lec 11-Spark Built-in Libraries.
noc19-cs33 Lec 12-Design of Key-Value Stores.
noc19-cs33 Lec 13 Data Placement Strategies.
noc19-cs33 Lec 14 CAP Theorem.
noc19-cs33 Lec 15 Consistency Solutions.
noc19-cs33 Lec 16 Design of Zookeeper.
noc19-cs33 Lec 17 CQL (Cassandra Query Language).
noc19-cs33 Lec 18 Design of HBase.
noc19-cs33 Lec 19 Spark Streaming and Sliding Window Analytics (Part-I).
noc19-cs33 Lec 20 Spark Streaming and Sliding Window Analytics (Part-II).
noc19-cs33 Lec 21 Sliding Window Analytics.
noc19-cs33 Lec 22 Introduction to Kafka.
noc19-cs33 Lec 23 Big Data Machine Learning (Part-I).
noc19-cs33 Lec 24 Big Data Machine Learning (Part-II).
noc19-cs33 Lec 25 Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics.
noc19-cs33 Lec 26 Parallel K-means using Map Reduce on Big Data Cluster Analysis.
noc19-cs33 Lec 27 Decision Trees for Big Data Analytics.
noc19-cs33 Lec 28 Big Data Predictive Analytics (Part-I).
noc19-cs33 Lec 29 Big Data Predictive Analytics (Part-II).
noc19-cs33 Lec 30 Parameter Servers.
noc19-cs33 Lec 31 PageRank Algorithm in Big Data.
noc19-cs33 Lec 32 Spark GraphX & Graph Analytics (Part-I).
noc19-cs33 Lec 33 Spark GraphX & Graph Analytics (Part-II).
noc19-cs33 Lec 34 Case Study: Flight Data Analysis using Spark GraphX.