JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To orient on the importance of ever-increasing volume, variety and velocity of data in organization and application of data analytical tools for decision making.
• To explain the different descriptive statistical measures.
• To impart knowledge on the aspects of predictive analytics.
• To provide understanding of the scope of data mining, regression trees, logistical regression.
• To elaborate on various applications of simulation in business.

Course Outcomes

Students will be able to:

• Understand the importance of business analytics in practice.
• Learn various rural marketing strategies
• Learn challenges of data modelling.
• Understand the aspects data mining.
• Learn Monte Carlo simulation, risk analysis and decision tree analysis.

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 Technique, 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.

Suggested Readings:

• James E.Sallis, Geir Gripsrud, Ulf Henning Olsson,Ragnhild Silkoset,Research Methods and Data Analysis for Business Decisions: A Primer Using SPSS,Springer International Publising,1e,2021.
• Anil Maheswari, Big Data, Tata McGraw Hill, New Delhi, 2e, 2019.
• James Evans, Business Analytics, Pearson Education,2e, 2017.
• Camm, Cochran, Fry, Ohlmann, Anderson, Sweeney, Williams Essential of Business Analytics, Cengage Learning, 2015.
• Foster Provost and Tom Fawcett, Data Science for Business, Shroff Publisher, 2018.
• Seema Acharya & Subhashini Chellappan: Big Data and Analytics, Wiley Publications, New Delhi, 2015.
• Thomas Eri, Wajid Khattack& Paul Buhler: Big Data Fundamentals, Concepts, drivers and Techniques by Prentice Hall of India, New Delhi, 2015.  31 MBA SEMESTER - IV