UNIQUE SERIES AKTU Syllabus
BADS601

Data Analytics

L-T-P3-1-0 Credits4 Session2024-25 TypeTheory
Appears in 2 semesters · 2 branches

B.Tech Artificial Intelligence & Data Science · Sem 6 (Core) · B.Tech CSE (Artificial Intelligence) · Sem 6 (Core)

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Syllabus

5 units · 40 lecture hours
Unit1

Unit 1: Introduction to Data Analytics

8 hrs

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.

Unit2

Unit 2: Data Analysis

8 hrs

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.

Unit3

Unit 3: Mining Data Streams

8 hrs

Introduction to streams concepts, stream data model and architecture, stream computing, sampling data in a stream, filtering streams, counting distinct 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.

Unit4

Unit 4: Frequent Itemsets and Clustering

8 hrs

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 noneuclidean space, clustering for streams and parallelism.

Unit5

Unit 5: Frame Works and Visualization

8 hrs

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..

Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Discuss various concepts of data analytics pipelineK1, K2
  2. CO2Apply classification and regression techniquesK3
  3. CO3Explain and apply mining techniques on streaming dataK2, K3
  4. CO4Compare different clustering and frequent pattern mining algorithmsK4
  5. CO5Describe the concept of R programming and implement analytics on Big data using R.K2, K3

Text books

  1. and References:
  2. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer
  3. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press.
  4. Bill Franks, Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with Advanced Analytics, John Wiley & Sons.
  5. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley
  6. David Dietrich, Barry Heller, Beibei Yang, “Data Science and Big Data Analytics”, EMC Education Series, John Wiley
  7. Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley and SAS Business Series
  8. Colleen Mccue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”, Elsevier
  9. Anil Maheshwari, “Data Analytics”, McGraw Hill Education
  10. Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, McGraw Hill
  11. Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning", Springer
  12. Mark Gardner, “Beginning R: The Statistical Programming Language”, Wrox Publication
  13. Pete Warden, Big Data Glossary, O’Reilly
  14. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons
  15. Pete Warden, Big Data Glossary, O’Reilly.
  16. Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan, "Handbook of Big Data", CRC Press
  17. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier

Official AKTU syllabus

This subject is on page 28, 29 of the original AKTU PDF.

Official AKTU syllabus PDF ↗