Course Objectives: This course enables the students to: Course Outcomes (COs): Course Course outcome (at course level) Learning and teaching strategies Assessment Strategies Paper Code Paper Title CLO 156.Formulate a problem and an abstract model to handle Big Data in business domain. CLO 157.Install Big Data tool/s like Hadoop for business analytics. CLO 158.Develop a data store to handle massive business data using Big Data tools and generate queries. CLO 159.Build a machine learning model on Big Data for business problems CLO 160.Examine the outcomes of Big Data based machine learning models and communicate the results. CLO 161.Evaluate the performance of models using metrics like confusion matrix, accuracy ,RMSE etc Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study Learning activities for the students: Self-learning assignments, presentations Class test, Semester end examinations, Quiz, Assignments, Presentation MBB 422 Big Data and Data Analytics
Understanding Big Data Digital data and its classification, characteristics of data, evolution and definition of big data. Challenges with big data, why big data, Traditional Business intelligence versus Big Data Big Data Analytics What is Big data analytics, why sudden hype around big data analytics, classification of analytics, top challenges facing big data, terminologies used in big data environment, Top analytics tools
Big Data Technology Landscape Apache Hadoop,Why Hadoop, Comparison with other systems: RDBMS, Grid computing, Hadoop overview, HDFS and its ecosystems, Hadoop architecture and 2.x core components. Managing Resources and applications with Hadoop YARN (Yet Another Resource Negotiator), Understanding MapReduce Programming, Running sample MapReduce program, Executing MapReduce Applications -Word count, Tera Sort, Radix Sort. Introduction to Hadoop Ecosystem, Pig, Hive, Sqoop, HBase.
Pig: Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Pig on Hadoop Hive: Hive Shell, Architecture, data types, Comparison with Traditional Databases, HiveQL, Tables, User Defined Functions.
NoSQL: Use of NoSQL, Types of NoSQL, Advantages of NoSQL. Use of No SQL in Industry, NoSQL Vendors, SQL versus NoSQL, NewSQL Hbase: Hbase basics, Concepts, Clients, Example, Hbase Versus RDBMS.
Machine Learning using python ,Python installation (Window and Ubuntu), Execution modes of Python ,Executing Python programs on hadoop, Python Libraries and Tools - Pandas for data analysis, Matplotlib for data visualization, Numpy for matrix processing, SciPy for image manipulation. Applications of Machine Learning, Implementation of machine learning in Hadoop environment. *Case studies related to entire topics are to be taught