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Sc. IT Part I Semester II)","Syllabus for M. Sc. Information Technology (Part I), Semester II under Mahatma Education Society’s Pillai College of Arts, Commerce & Science (Autonomous), affiliated to the University of Mumbai. Covers the course “Big Data Analytics” with core theory and practical components, assigned theory and practical marks and credits. Includes objectives, course structure, and unit-wise analytical and technological topics such as data analytics lifecycle, clustering and association, Hadoop architecture, TF-IDF and sentiment analysis, MapReduce, Spark with PySpark, and distributed data tools. Practical includes Hadoop/HDFS setup, MapReduce implementations, HBase/MongoDB workflows, Hive configuration, Jaql illustration, and classification/regression exercises using tools and datasets.","Mahatma Education Society’s  \nPillai College of Arts, Commerce & Science  \n(Autonomous)  \nAffiliated to University of Mumbai  \nNew Panvel  \nSyllabus for M. Sc. IT Part I Semester II Program: M. Sc. Information Technology  \nSemester based Credit and Grading system for the  \nacademic year 2019-20)  \n\n| Semester II |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Course Code | Cours\u003Cbr>e\u003Cbr>Type | Course Title | Theory/\u003Cbr>Practical | Marks | Credits | Lectures\u003Cbr>/Week |\n| PMSIT201 | Core | Big Data\u003Cbr>Analytics | Theory | 100 | 4 | 4 |\n| PMSIT202 | Core | Modern Networking | Theory | 100 | 4 | 4 |\n| PMSIT203 | Core | Microservices Architecture | Theory | 100 | 4 | 4 |\n| PMSIT204 | Core | Image Processing | Theory | 100 | 4 | 4 |\n| PMSIT201P | Core | PMSIT201 | Practical | 50 | 2 | 4 |\n| PMSIT202P | Core | PMSIT202 | Practical | 50 | 2 | 4 |\n| PMSIT203P | Core | PMSIT203 | Practical | 50 | 2 | 4 |\n| PMSIT204P | Core | PMSIT204 | Practical | 50 | 2 | 4 |\n| Total |  |  |  | 600 | 24 |  |\n\n\n| BOS | Information Technology |\n| --- | --- |\n| Class | M. Sc.I.T. |\n| Semester | II |\n| Subject | BigData Analytics |\n| Subject Code | PMSIT201 |\n| Level of the Subject | Advance |\n\nObjectives:  \n1. To provide an overview of an exciting growing field of big data analytics.  \n2. To introduce the tools required to manage and analyze big data like Hadoop, NoSqlMapReduce.  \n\n| Unit\u003Cbr>No. | Name of Unit | Topi\u003Cbr>c No. | Content | No. of\u003Cbr>Lectures |\n| --- | --- | --- | --- | --- |\n| 1 | Introduction to Big Data Analytics and Data Analytics Lifecycle | 1.1 | Introduction to Big Data, Characteristics of Data, and Big Data Evolution of Big Data, Definition of Big Data, Challenges with big data, Why Big data? Data Warehouse environment, Traditional Business Intelligence versus Big Data. State of Practice in Analytics, Key roles for New Big Data Ecosystems, Examples of big Data Analytics. | 15L |\n|  |  | 1.2 | Big Data Analytics, Introduction to big data analytics, Classification of Analytics, Challenges of Big Data, Importance of Big Data, Big Data Technologies, Data Science, Responsibilities, Soft state eventual consistency. Data Analytics Life Cycle |  |\n| 2 | Analytical Theory and Methods: Clustering, Association | 2.1 | Analytical Theory and Methods: Clustering Associated Algorithms, Association Rules, Apriori Algorithm, Candidate Rules, Applications of Association Rules, Validation and Testing, Diagnostics | 15L |\n\n\n|  | Rules and Regression | 2.2 | Regression, Linear Regression, Logistic\u003Cbr>Regression, Additional Regression Models. |  |\n| --- | --- | --- | --- | --- |\n| 3 | Analytical Theory and Methods and Hadoop Architecture | 3.1 | Analytical Theory and Methods: Classification, Decision Trees, Naïve Bayes, Diagnostics of Classifiers, Additional Classification Methods, Time Series Analysis, Box Jenkins methodology, ARIMA Model, Additional methods. Text Analysis, Steps,\u003Cbr>Text Analysis Example, Collecting Raw Text, Representing Text, Term Frequency-Inverse Document Frequency (TFIDF), Categorizing Documents by Topics, Determining Sentiments. | 15L |\n|  |  | 3.2 | Data Product, Building Data Products at Scale with Hadoop, Data Science Pipeline and Hadoop Ecosystem, Operating System for Big Data, Concepts, Hadoop Architecture, Working with Distributed file system, Working with Distributed Computation. |  |\n| 4 | Advanced AnalyticsTechnology and Tools | 4.1 | Framework for Python and Hadoop Streaming, Hadoop Streaming, MapReduce with Python, Advanced MapReduce. In-Memory Computing with Spark, Spark Basics, Interactive Spark with PySpark, Writing Spark Applications. | 15L |\n|  |  | 4.2 | Distributed Analysis and Patterns, Computing with Keys, Design Patterns, Last-Mile Analytics, Data Mining and Warehousing, Structured Data Queries with Hive, HBase, Data Ingestion, Importing Relational data with Sqoop, Injesting stream data with flume. Analytics with higher level APIs, Pig, Spark’s higher level APIs. |  |\n\nExpected Outcome:  \n1. ","cbCaieNfLcI9TwUn","https://ap.wps.com/l/cbCaieNfLcI9TwUn","pdf",404190,22,"English","en",105,"# Course Overview\n## Objectives\n# Course Structure\n## Theory Courses\n## Practical Courses\n# Theory Units\n## Unit 1: Big Data Analytics and Life Cycle\n## Unit 2: Clustering and Association Rules\n## Unit 3: Hadoop Architecture and Analytics Methods\n## Unit 4: Advanced Technologies and Tools\n# Expected Outcome\n# Reference Books\n# Practical Program","[{\"question\":\"What are the learning objectives of the Big Data Analytics course?\",\"answer\":\"The syllabus aims to provide an overview of big data analytics and introduce tools and technologies for managing and analyzing big data such as Hadoop and related frameworks.\"},{\"question\":\"Which topics are covered in the theory units?\",\"answer\":\"Theory units include analytics lifecycle concepts, clustering and association rules (including Apriori), regression and classification methods, Hadoop architecture, TF-IDF and sentiment-related text analysis, and frameworks for Python streaming, MapReduce, and Spark/PySpark.\"},{\"question\":\"What practical tasks are included for Big Data Analytics?\",\"answer\":\"Practical work covers installing and exploring Hadoop/HDFS, implementing MapReduce programs (including word count and dataset processing), storing and manipulating data in systems like HBase/MongoDB using R/Python, configuring Hive, demonstrating Jaql, and performing decision tree/SVM classification plus regression modeling.\"}]","MSCIT-SEM-II - Autonomous Syllabus - Big Data Analytics (M. 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