Exploratory Data Analytics and Data Visualization

Course Objectives :

1. To understand the need for Data Mining and advantages to the business world.
2. To get a clear idea of various classes of Data Mining techniques, their need, scenarios (situations) and scope of their applicability
3. To learn the algorithms used for various type of Data Mining problems
4. To understand how to explore and communicate data using data visualization techniques

Course Outcomes (CO)

1. CO 1 - Describe the life cycle phases of Data Analytics through discovery, planning and building.
2. CO 2 - Understand and apply Data Analysis Techniques.
3. CO 3 - Implement various Data streams.
4. CO 4 - Understand item sets, Clustering, frame works & Visualizations.

UNIT-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, and operationalization.

UNIT-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.

UNIT-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.

UNIT – IV

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.

Textbook(s):

1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer.
2. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, ElsevierRobert

References:

1. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press.
2. David Dietrich, Barry Heller, Beibei Yang, “Data Science and Big Data Analytics”, John Wiley