Department Events
DATA ANALYTICS AND DATA VISUALIZATION
Max. Marks: 100.00 Course Objectives: This course enables the students to 6. Construct cases and new ideas where the knowledge of data analytics and visualization can be implemented. Course Outcomes(COs): Learning Outcome (at course level) Learning and teaching strategies Assessment Strategies CO212. Define the concepts of data analytics. CO213. Describe the concepts of inferential statistics and descriptive analytics. CO214. Execute different predictive and prescriptive methods used in data analytics CO215. Demonstrate the concept of data visualization. CO216. Evaluate using different directory of visualizations. CO217. Differentiate between data analytics, data science and business intelligence. CO218. Construct cases and new ideas where the knowledge of data analytics and visualization can be implemented. 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 · Assignments · Written test in classroom · Classroom activity · Continues Assessment · Semester End Examination
Introduction 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, Pattern Recognition, Types of data analytics, Introduction to data visualization.
Inferential Statistics And Descriptive Analysis Statistical concepts, Descriptive Analysis, Sampling distributions, resembling, 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.
Predictive & Prescriptive Analytics Predictive Analytics, Supervised, Unsupervised learning , Neural networks, Kohonen models, Normal, Deviations from normal patterns, Normal behaviors, Expert options, Variable entry, Mining Frequent item sets, Market based model, Apriori Algorithm, Handling large data sets in Main memory, Limited Pass algorithm, Clustering Techniques, Hierarchical , K- Means, Clustering high dimensional data Prescriptive Analytics: Basics of Prescriptive Analytics, Optimization models, Decision Trees
Introduction to Data Visualization Data visualization, Need for Visualization, Introduction to tools need for data visualization, Mapping Data onto Aesthetic, Aesthetics and types of data, Scales maps data values onto Aesthetics, Coordinate Systems and Axes, Cartesian Coordinates, Nonlinear Axes, Coordinate Systems with Curved Axes, Color Scales, Color as a tool to distinguish, Color to represent data values, Color as a tool to Highlight
Directory of Visualizations Directory of Visualizations: Amounts, Distributions, Proportions, x-y relationships, Geospatial Data, Visualizing Amounts: Bar Plots, Grouped and Stacked Bars, Dot Plots and Heat Maps. Visualizing Distributions: Histograms and Density Plots, Visualizing a Single Distribution, Visualizing Multiple Distributions at the same time.
