Verify Account

SYLLABUS

Unit-I

Introduction to Data analytics Introduction to Data – Importance of Analytics – Data for Business Analytics – Big Data – Business Analytics in Practice. Data Visualization – Data Visualization Tools, Data Queries, Statistical Methods for Summarizing Data, Exploring Data using Pivot Tables.

Unit-II

Descriptive statistical measures Population and Samples, Measures of Location, Measures of Dispersion, Measures of Variability, Measures of Association. Probability Distribution and Data Modeling – Discrete Probability Distribution, Continuous Probability Distribution, Random Sampling from Probability Distribution, Data Modeling and Distribution Fitting.

Unit-III

Predictive Analytics Karl Pearson Correlation Techniques – Multiple Correlation – Spearman’s Rank Correlation – Simple and Multiple Regression – Regression by the Method of Least Squares – Building Good Regression Models – Regression with Categorical Independent Variables – Linear Discriminant Analysis – One-way and Two-way ANOVA.

Unit-IV

Data mining Scope of Data Mining, Data Exploration and Reduction, Unsupervised Learning – Cluster Analysis, Association Rules, Supervised Learning – Partition Data, Classification Accuracy, Prediction Accuracy, k-nearest Neighbors, Classification and Regression Trees, Logistics Regression.

Unit-V

Simulation Random Number Generation, Monte Carlo Simulation, What if Analysis, Verification and Validation, Advantages and Disadvantages of Simulation, Risk Analysis, Decision Tree Analysis.