IS4071 Data Analytics Syllabus:
IS4071 Data Analytics Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
1. Recognize the importance of data analytics
2. Exhibit competence on data analytics packages
3. Apply solution methodologies for industrial problems.
UNIT I INTRODUCTION
Introduction to Multivariate Statistics-Degree of Relationship among Variables-Review of Univariate and Bivariate Statistics-Screening Data Prior to Analysis-Missing Data, Outliers, Normality, Linearity, and Homoscedasticity.
UNIT II MULTIPLE REGRESSION
Multiple Regression- Linear and Nonlinear techniques- Backward-Forward-Stepwise Hierarchical regression-Testing interactions (2way interaction) – Analysis of Variance and Covariance (ANOVA & ANCOVA) – Multivariate Analysis of Variance and Covariance (MANOVA & MANCOVA).
UNIT III LOGISTIC REGRESSION
Regression with binary dependent variable -Simple Discriminant Analysis Multiple Discriminant analysis-Assessing classification accuracy- Conjoint analysis (Full profile method).
UNIT IV PRINCIPAL COMPONENT ANALYSIS
Principal Component Analysis -Factor Analysis- Orthogonal and Oblique Rotation-Factor Score Estimation-Multidimensional Scaling-Perceptual Map-Cluster Analysis (Hierarchical Vs Nonhierarchical Clustering).
UNIT V
Latent Variable Models an Introduction to Factor, Path, and Structural Equation Analysis- Time series data analysis (ARIMA model) – Decision tree analysis (CHAID, CART) – Introduction to Big Data Management.
COURSE OUTCOMES:
On completion of the course, the student will be able to:
To recognize the importance of data analytics
To Exhibit competence on data analytics packages
To apply solution methodologies for industrial problems.
REFERENCES:
1. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. “Multivariate data analysis”, (7th edition). Pearson India. 2015
2. Tabachnick, B. G., & Fidell, L. S., “Using multivariate statistics”, (5th edition). Pearson Prentice Hall, 2001
3. Gujarati, D. N. , “Basic econometrics”, Tata McGraw-Hill Education, 2012
4. Malhotra, N. K., “ Marketing research: An applied orientation”, 5/e. Pearson Education India, 2008
5. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. “ Applied multiple regression/correlation analysis for the behavioral sciences”, Routledge., 2013
6. Han, J., Kamber, M., & Pei, J. “Data mining: concepts and techniques: concepts and techniques”, Elsevier, 2011.