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AKTU Introduction to Data Analytics and Visualization (BCDS501) Syllabus PDF – B.Tech 3rd Year
Introduction to Data Analytics and Visualization BCDS501
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Official AKTU syllabus of Introduction to Data Analytics and Visualization (BCDS501) 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 — Introduction to Data Analytics and Visualization
Download PDFBCDS501 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of course, the student will be able to understand
| CO 1 | Describe the life cycle phases of Data Analytics through discovery, planning and building. | K 3 |
| CO 2 | Understand and apply Data Analysis Techniques. | K , K 3 4 |
| CO 3 | Implement various Data streams. | K , K 2 3 |
| CO 4 | Understand item sets, Clustering, frame works & Visualizations. | K , K 2 4 |
| CO 5 | Understand the Data Visualizations & Human Vision | K , K 2 3 |
Course Content
L-T-P: 3-1-0
| 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, 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. | 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 | Introduction to Visualization and Stages – Computational Support – Issues – Different Types of Tasks – Data representation – Limitation: Display Space- Rendering Time – Navigation Links. Human Vision – Space Limitation – Time Limitations – Design – Exploration of Complex Information Space – Figure Caption in Visual Interface – Visual Objects and Data Objects - Space Perception and Data in Space – Images, Narrative and Gestures for Explanation. | 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.
- Bill Franks, Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with Advanced Analytics, John Wiley & Sons.
- Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence 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
- Anil Maheshwari, “Data Analytics”, McGraw Hill Education
- Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class Hadoop 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, ElsevierRobert Spence, “Information Visualization Design for Interaction”, Second Edition, Pearson Education, 2006.
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