Department Events
Data Analytics
The course will enable the students to
1 Define the concepts of data analytics.
2 Understand the concepts of inferential statistics and descriptive analytics
3 Demonstrate the concept of predictive analytics.
4 Differentiate between business intelligence and data analytics.
5 Evaluate using different predictive and prescriptive methods
6 Construct cases and new ideas where the knowledge of data analytics can be implemented.
Course Learning Outcomes (CLOs):
Learning Outcome (at course level) Students will be able to: | Learning and teaching strategies | Assessment Strategies |
| Approach in teaching: Interactive Lectures, Modeling, Discussions, implementing enquiry based learning, Student centered approach, Through audio-visual aids
Learning activities for the students: Experiential Learning, Presentations, Discussions, Quizzes and Assignments
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Introduction to Big Data Platform – Challenges of conventional systems - Web data , Evolution of Analytic scalability, Analytic approaches, Business Approaches, Analytic Innovation, Traditional approaches- iterative, Analysis vs. reporting – Overview of data analytic tools- NodeXL, Pentaho
Statistical concepts, Descriptive Analysis, Sampling distributions, resampling, statistical inference, prediction error.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. Visualizations - Visual data analysis using Datawrapper
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
Predictive Analytics – Supervised – Unsupervised learning – Neural networks – Kohonen models – Normal – Deviations from normal patterns – Normal behaviours – Expert options – Variable entry - Mining Frequent item sets - Market based model – 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
Perspective Analytics: Basics of Perspective Analytics, Optimization models, Decision Trees
Basic of R, concepts before starting, Working of R - Creating, listing and deleting the objects in memory - The on-line help Data with R Objects, R data Frames and Matrices, Reading data in a file , Saving data, Generating data, Manipulating objects Graphics with R Managing graphics , Graphical functions - Low-level plotting commands, Graphical parameters, A practical example - The grid and lattice packages
- Thomas A. Runkler, “Data Analytics: Models and Algorithms for Intelligent Data Analysis”, Second Edition, Wiley, Springer Vieweg, 2012
- VigneshPrajapat, “Big Data Analytics with R and Hadoop”, PACKT Publishing, 2014
- Dr. Anasse Bari, Mohamed Chaouchi, Tommy Jun, “Predictive Analytics For Dummies”, Second Edition, Wiley, 2016
- Michel Berthold, David J Hand, “Intelligent Data Analysis”, Springer 2007
- Chris Eaton, Dirk De Roos, Tomeutsch, George Lapis, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, McGraw Hill Publishing, 2012
- AnandRajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2012.
- Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics”, Wiley and SAS Business Series, 2012.
- Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, McGraw Hill, 2011.
- Minelli, Michelle Chambers, and AmbigaDhiraj, “Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses”, Wiley, 2013.
