Aktu Btech Data Analytics (KCS-051/KIT-601) Syllabus

Learn about the techniques and tools used in the AKTU B.Tech syllabus for Data Analytics, and discover how to draw conclusions from huge datasets. Activate the potential of data-driven decision-making.

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UNIT-1: INTRODUCTION TO DATA ANALYTICS

• 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 

UNIT-2: 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. 

UNIT-3: MINING DATASTREAMS 

• 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.

UNIT-4: FREQUENT ITEMSETS & CLUSTERING 

• Mining frequent itemsets,
• Market based modelling,
• Apriori algorithm,
• Handling large data sets in main memory,
• Limited pas 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 & parallelism. 

UNIT-5: FRAME WORKS & VISUALIZATION

• 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.

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