ࡱ> [ 3bjbj 8ΐΐ* 8tUS1L}(RRRRRRR$UXRR4R)!)!)!lR)!R)!)!JXi,N`KDsRR0S7LYNYXNNYP)!RR)!SY : @Program(s)B Tech [CSE]Academic Session, SemesterAutumn, 2016 , 7th SemSubject NameData AnalyticsSubject CodeIT- 3002 Teachers : Dr. Bhabani Shankar Prasad Mishra, Dr Siddharth Swarup Rautaray, Dr Manjusha Pandey IT-3004 DATA ANALYTICS Cr-4 COURSE OBJECTIVES To understand the concept of data analytics To explore tools and practices for working with big data To understand how data analytics can leverage into a key component To understand how to mine the data To learn about stream computing To know about the research that requires the integration of large amounts of data COURSE OUTCOMES Identify the need for data analytics for different domains Performing analysis of data using R tool. Use of Hadoop, Map Reduce Framework Apply data analytics for a give problem Contextually integrate and correlate large amounts of information automatically to gain faster insights SYLLABUS INTRODUCTION TO BIG DATA (9Hrs) Importance of Data, Characteristics of Data Analysis of Unstructured Data, Combining Structured and Unstructured Sources. Introduction to Big Data Platform Challenges of conventional systems Web data Evolution ofAnalytic scalability, analytic processes and tools, Analysis vs reporting Modern data analytic tools, Types of Data, Elements of Big Data, Big Data Analytics, Data Analytics Lifecycle. Exploring the Use of Big Data in Business Context, Use of Big Data in Social Networking, Business Intelligence, Product Design and Development DATA ANALYSIS (10Hrs) Exploring R: Exploring Basic Features of R, Programming Features, Packages, Exploring RStudio, Handling Basic Expressions in R, Basic Arithmetic in R, Mathematical Operators, Calling Functions in R, Working with Vectors, Creating and Using Objects, Handling Data in R Workspace, Creating Plots, Using Built-in Datasets in R, Reading Datasets and Exporting Data from R, Manipulating and Processing Data in R, Statistical Features-Analysis of time series: linear systems analysis, nonlineardynamics Rule induction Neural networks: learning and generalization, competitive learning,principal component analysis and neural networks. BIG DATA TECHNOLOGY FOUNDATIONS & MINING DATA STREAMS 10Hrs) Exploring the Big Data Stack, Data Sources Layer, Ingestion Layer, Storage Layer, Physical Infrastructure Layer, Platform Management Layer, Security Layer, Monitoring Layer, Analytics Engine, Visualization Layer, Big Data Applications, Virtualization. Introduction to Streams Concepts Stream data model and architecture Stream Computing,Sampling data in a stream Filtering streams, Counting distinct elements in a stream. FREQUENT ITEMSETS AND CLUSTERING (9Hrs) Mining Frequent itemsets Market based model Apriori Algorithm Handling large data sets in Mainmemory Limited Pass Algorithm Counting frequent itemsets in a stream Clustering Techniques Hierarchical K- Means. Analytical Approaches and Tools to Analyze Data: Text Data Analysis, Graphical User Interfaces, Point Solutions. FRAMEWORKS AND VISUALIZATION (10Hrs) Distributed and Parallel Computing for Big Data, MapReduce Hadoop, Hive, MapR Hadoop -YARN - Pig and PigLatin, Jaql - Zookeeper - HBase, Cassandra- Oozie, Lucene- Avro, Mahout. Hadoop Distributed filesystems Visualizations Visual data analysis techniques, interaction techniques; Systems andapplications. TEXT BOOKS: Big Data, Black Book, DT Editorial Services, Dreamtech Press, 2015 Big Data and Analytics, Seema Acharya, Subhashini Chellappan, Infosys Limited, Publication: Wiley India Private Limited,1st Edition 2015 REFERENCES: Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams withadvanced analystics, John Wiley & sons, 2012. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007 Pete Warden, Big DataGlossary,OReilly, 2011. Jiawei Han, MichelineKamber Data Mining Concepts and Techniques, Second Edition, Elsevier,Reprinted 2008. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services(Editor), Wiley, 2014 Stephan Kudyba, Thomas H. Davenport, Big Data, Mining, and Analytics, Components of Strategic Decision Making, CRC Press, Taylor & Francis Group. 2014 Norman Matloff , THE ART OF R PROGRAMMING, No Starch Press, Inc.2011. Big Data For Dummies, HYPERLINK "http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Judith+Hurwitz"Judith Hurwitz,HYPERLINK "http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Alan+Nugent"Alan Nugent,HYPERLINK "http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Fern+Halper"Fern Halper,HYPERLINK "http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Marcia+Kaufman"Marcia Kaufman, Wiley 2013 LESSON PLAN ChaptersTopic/CoverageNo. of lecturesLecture serial no.1. INTRODUCTION TO BIG DATA Data Science Importance of Data Characteristics of Data Analysis of Unstructured Data Combining Structured and Unstructured Sources. Big Data Platform Challenges of conventional systems Web data Evolution ofAnalytic scalability, analytic processes and tools, Analysis vs reporting Modern data analytic tools.4 1-9Tutorial1Data Analytics Lifecycle.3Tutorial12. DATA ANALYSISExploring R: Exploring Basic Features of R, Exploring RStudio, Handling Basic Expressions in R, Basic Arithmetic and , Mathematical Operators in R, Calling Functions in R, Working with Vectors, Creating and Using Objects, Handling Data in R Workspace, Creating Plots, Reading Datasets and Exporting Data from R, Manipulating and Processing Data in R.410-19Tutorial1Statistical Features. Analysis of time series: linear systems analysis, nonlineardynamics Rule induction Neural networks: learning and generalization, competitive learning, principal component analysis and neural networks.3Tutorial13. BIG DATA TECHNOLOGY FOUNDATIONS & MINING DATA STREAMS Exploring the Big Data Stack, Data Sources Layer, Ingestion Layer, Storage Layer, Physical Infrastructure Layer, Platform Management Layer, Security Layer, Monitoring Layer, Analytics Engine, Visualization Layer, Big Data Applications, Virtualization. 4 20-29 Tutorial1Introduction to Streams Concepts Stream data model and architecture Stream Computing, Sampling data in a stream Filtering streams, Counting distinct elements in a stream. 3 Tutorial14. FREQUENT ITEMSETS AND CLUSTERING Mining Frequent itemsets Market based model Apriori Algorithm Handling large data sets in Mainmemory Limited Pass Algorithm Counting frequent itemsets in a stream Clustering Techniques Hierarchical K- Means. 4 30-38Tutorial1Analytical Approaches Tools to Analyze Data Text Data Analysis, Graphical User Interfaces 2Tutorial15. FRAMEWORKS AND VISUALIZATION Distributed and Parallel Computing for Big Data, MapReduce Hadoop, Hive, MapR Hadoop -YARN - Pig and PigLatin, Jaql - Zookeeper - HBase, Cassandra- Oozie, Lucene- Avro, Mahout. 439-48Tutorial1Hadoop Distributed filesystems Visualizations Visual data analysis techniques, interaction techniques; Systems andapplications. 3 Course Delivery Plan: DurationTopicsWeek- 1Introduction to Data ScienceWeek- 2Elements of Big DataWeek- 3Data Analytics LifecycleWeek- 4Exploring R programming featuresWeek- 5Manipulating and Processing Data in RWeek- 6Exploring the Big Data StackWeek- 7Analytics Engine & Virtualization Week- 8Introduction to Streams ConceptsWeek- 9Mining Frequent itemsetsWeek- 10Clustering TechniquesWeek-11Distributed and Parallel Computing for Big DataWeek-12Hadoop Framework & Implementation Assessment: Assessment will be based on quizzes, class test and presentations. Evaluation Scheme: ExamMarksEnd Semester60Internal Mid Semester25Assignment/Quiz15Total100 Program Educational Objectives PEO-1. To lead a successful career in industries or pursue higher studies or entrepreneurial endeavors. PEO-1. To offer techno-commercially feasible and socially acceptable solutions to real life engineering problems. PEO-1. To demonstrate effective communication skill, professional attitude and a desire to learn. Program Outcomes Ability to apply knowledge of mathematics, science, engineering, computing to solve complex problems. Ability to identify, analyze and solve complex software and hardware engineering problems. Ability to design, implement and evaluate various computer based systems to meet the needs of the society by considering public health, safety, cultural, societal and environmental issues. Ability to design & conduct experiments and interpret data. Ability to use techniques, skills and modern engineering and IT tools to various relevant engineering practices. Ability to examine and understand the impact of societal, health, safety, legal and cultural concerns at local, national and international levels relevant to engineering practices. Ability to recognize the sustainability and environmental impact of the computer-based engineering solutions. Ability to follow prescribed norms, responsibilities and ethics in engineering practices. Ability to work effectively as an individual and in a team. Ability to communicate effectively through oral, written and pictorial means with engineering community and the society at large. Ability to recognize the need for and to engage in life-long learning. Ability to understand and apply engineering & management principles in executing projects. Course Outcomes (CO) of Data Analytics Able to understand the emergence and importance of Big data Science along with necessity of big data analysis. Able to understand the working of R-tool and write structured and well-commented scripts in R to analyze the data set with an ability to test and debug them in the laboratory. Able to understand the Big data stack with layered and the concepts related to Big data technologies and its applications. Able to understand the concepts of Data mining and usage of data mining techniques for analysis of big data. Able to have an understanding framework and visualization of hadoop architecture and its applications. 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