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AKTU Data Analytics (BIT601) Syllabus PDF – B.Tech 3rd Year
Data Analytics BIT601
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Official AKTU syllabus of Data Analytics (BIT601) for B.Tech 3rd Year – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Data Analytics
Download PDFBIT601 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of course , the student will be able to
| CO 1 | Discuss various concepts of data analytics pipeline | K , K 1 2 |
| CO 2 | Apply classification and regression techniques | K 3 |
| CO 3 | Explain and apply mining techniques on streaming data | K , K 2 3 |
| CO 4 | Compare different clustering and frequent pattern mining algorithms | K 4 |
| CO 5 | Describe the concept of R programming and implement analytics on Big data using R. | K ,K 2 3 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction to Data Analytics: Sources and nature of data, classification of data (structured, semi-structured, unstructured), characteristics of data, introduction to Big Data platform, need of data analytics, evolution of analytic scalability, analytic process and tools, analysis vs reporting, modern data analytic tools, applications of data analytics. Data Analytics Lifecycle: Need, key roles for successful analytic projects, various phases of data analytics lifecycle – discovery, data preparation, model planning, model building, communicating results, operationalization. | 08 |
| II | Data Analysis: Regression modeling, multivariate analysis, Bayesian modeling, inference and Bayesian networks, support vector and kernel methods, analysis of time series: linear systems analysis & nonlinear dynamics, rule induction, neural networks: learning and generalisation, competitive learning, principal component analysis and neural networks, fuzzy logic: extracting fuzzy models from data, fuzzy decision trees, stochastic search methods. | 08 |
| III | Mining Data Streams: Introduction to streams concepts, stream data model and architecture, stream computing, sampling data in a stream, filtering streams, countingdistinct elements in a stream, estimating moments, counting oneness in a window, decaying window, Real-time Analytics Platform ( RTAP) applications, Case studies – real time sentiment analysis, stock market predictions. | 08 |
| IV | Frequent Itemsets and Clustering: Mining frequent itemsets, market based modelling, Apriori algorithm, handling large data sets in main memory, limited pass algorithm, counting frequent itemsets in a stream, clustering techniques: hierarchical, K-means, clustering high dimensional data, CLIQUE and ProCLUS, frequent pattern based clustering methods, clustering in non-euclidean space, clustering for streams and parallelism. | 08 |
| V | Frame Works and Visualization: MapReduce, Hadoop, Pig, Hive, HBase, MapR, Sharding, NoSQL Databases, S3, Hadoop Distributed File Systems, Visualization: visual data analysis techniques, interaction techniques, systems and applications. Introduction to R - R graphical user interfaces, data import and export, attribute and data types, descriptive statistics, exploratory data analysis, visualization before analysis, analytics for unstructured data. | 08 |
Text Books
- Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer
- Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press.
- John Garrett,Data Analytics for IT Networks : Developing Innovative Use Cases, Pearson Education Computer Science and Information Technology, IT (V & VI semester)
- Bill Franks, Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with AdvancedAnalytics, John Wiley & Sons.
- Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging BusinessIntelligence and Analytic Trends for Today's Businesses", Wiley
- David Dietrich, Barry Heller, Beibei Yang, “Data Science and Big Data Analytics”, EMC Education Series,John Wiley
- Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley and SAS Business Series
- Colleen Mccue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”,Elsevier
- Michael Berthold, David J. Hand,” Intelligent Data Analysis”, Springer
- Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise ClassHadoop and Streaming Data”, McGraw Hill
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning", Springer
- Mark Gardner, “Beginning R: The Statistical Programming Language”, Wrox Publication
- Pete Warden, Big Data Glossary, O’Reilly
- Glenn J. Myatt, Making Sense of Data, John Wiley & Sons
- Pete Warden, Big Data Glossary, O’Reilly.
- Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan, "Handbook of Big Data", CRC Press
- Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier Computer Science and Information Technology, IT (V & VI semester)
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