AKTU Data Analytics (BCS052) Notes & Study Material PDF – B.Tech 3rd Year Sem 5

Complete and exam-oriented Data Analytics notes for AKTU B.Tech 3rd Year Semester V. Covers all 5 units with detailed concepts, Data Analytics Lifecycle, regression, Bayesian modeling, data streams, Apriori, clustering, Hadoop, MapReduce, visualization, R programming, solved numericals, important questions and AKTU-pattern sample paper.

This comprehensive study material provides topic-wise, in-depth coverage of the AKTU BCS-052 syllabus, including conceptual explanations, diagrams, solved numericals, important questions and examination-oriented preparation material. The attached PDF contains 145 pages.

🎓 Course Details

• Subject: Data Analytics
• Course Code: BCS-052
• Year: 3rd Year
• Semester: V
• Session: 2026–27
• Course Type: Departmental Elective-I
• Credits: 3
• Branches: CSE, Computer Science, Computer Engineering & Allied Branches

📚 Complete 5-Unit Coverage

🔹 UNIT I – Introduction to Data Analytics & Analytics Lifecycle

Topics include:

• Introduction to Data Analytics
• Sources and Nature of Data
• Structured, Semi-structured & Unstructured Data
• Characteristics of Data
• Big Data Platform
• Need for Data Analytics
• Evolution of Analytic Scalability
• Analytic Process and Tools
• Analysis vs Reporting
• Modern Data Analytic Tools
• Applications of Data Analytics
• Data Analytics Lifecycle
• Discovery
• Data Preparation
• Model Planning
• Model Building
• Communicating Results
• Operationalization

🔹 UNIT II – Data Analysis Techniques

Covers:

• Regression Modeling
• Multivariate Analysis
• Bayesian Modeling & Inference
• Bayesian Networks
• Support Vector Machines
• Kernel Methods
• Time Series Analysis
• Linear Systems Analysis
• Nonlinear Dynamics
• Rule Induction
• Neural Networks
• Learning & Generalisation
• Competitive Learning
• Principal Component Analysis
• Fuzzy Logic
• Fuzzy Decision Trees
• Stochastic Search Methods

🔹 UNIT III – Mining Data Streams

Includes:

• Data Stream Concepts
• Stream Data Model & Architecture
• Stream Computing
• Sampling Data in a Stream
• Filtering Streams
• Bloom Filter
• Counting Distinct Elements
• Flajolet-Martin Algorithm
• Estimating Moments
• Counting Ones in a Window
• DGIM Algorithm
• Decaying Window
• Real-Time Analytics Platform (RTAP)
• Real-Time Sentiment Analysis
• Stock Market Prediction Case Study

The detailed index also includes dedicated sections for Bloom Filter, Flajolet-Martin, DGIM, RTAP and the real-time case studies.

🔹 UNIT IV – Frequent Itemsets & Clustering

Topics include:

• Mining Frequent Itemsets
• Market-Basket Modelling
• Apriori Algorithm
• Handling Large Data Sets
• Limited-Pass Algorithms
• Frequent Itemsets in Streams
• Hierarchical Clustering
• K-Means Clustering
• High-Dimensional Data Clustering
• CLIQUE
• ProCLUS
• Frequent Pattern-Based Clustering
• Clustering in Non-Euclidean Spaces
• Clustering for Streams
• Parallel Clustering

🔹 UNIT V – Frameworks, Visualization & Introduction to R

Covers:

• MapReduce
• Hadoop
• Hadoop Distributed File System (HDFS)
• Pig
• Hive
• HBase
• MapR
• Sharding
• NoSQL Databases
• Amazon S3
• Visual Data Analysis
• Interaction Techniques
• Visualization Systems & Applications
• Introduction to R
• R Graphical User Interfaces
• Data Import & Export
• Attribute & Data Types
• Descriptive Statistics
• Exploratory Data Analysis
• Data Visualization
• Analytics for Unstructured Data

⭐ What You Get

✅ 145 Pages Complete Study Material
✅ Complete 5-Unit Coverage
✅ Topic-wise Detailed Notes
✅ Data Analytics Lifecycle
✅ Big Data & Analytics Concepts
✅ Data Analysis Techniques
✅ Data Stream Mining
✅ Apriori & Frequent Itemsets
✅ K-Means & Hierarchical Clustering
✅ Hadoop & MapReduce
✅ Hive, Pig & HBase
✅ NoSQL & Amazon S3
✅ Data Visualization
✅ R Programming & Data Analysis
✅ Diagrams & Illustrations
✅ Solved Numericals
✅ Important Questions
✅ Unit-wise Summaries & Glossary
✅ AKTU-Pattern Sample Question Paper

The index confirms dedicated unit summaries, glossaries, important questions and an AKTU-pattern sample question paper.