CP7003 DATA ANALYSIS AND BUSINESS INTELLIGENCE - ANNA UNIVERSITY 1ST SEM CSE SYLLABUS REG-2013

ANNA UNIVERSITY, CHENNAI

REGULATIONS - 2013

M.E. COMPUTER SCIENCE AND ENGINEERING

CP7003 DATA ANALYSIS AND BUSINESS INTELLIGENCE

OBJECTIVES: 
 To understand linear regression models
 To understand logistic regression models
 To understand generalized linear models
 To understand simulation using regression models
 To understand causal inference
 To understand multilevel regression
 To understand data collection and model understanding

UNIT I LINEAR REGRESSION
Introduction to data analysis – Statistical processes – statistical models – statistical inference –
review of random variables and probability distributions – linear regression – one predictor – multiple predictors – prediction and validation – linear transformations – centering and
standardizing – correlation – logarithmic transformations – other transformations – building
regression models – fitting a series of regressions

UNIT II LOGISTIC AND GENERALIZED LINEAR MODELS
Logistic regression – logistic regression coefficients – latent-data formulation – building a logistic regression model – logistic regression with interactions – evaluating, checking, and comparing fitted logistic regressions – identifiability and separation – Poisson regression – logistic-binomial model – Probit regression – multinomial regression – robust regression using t model – building complex generalized linear models – constructive choice models.

UNIT III SIMULATION AND CAUSAL INFERENCE
Simulation of probability models – summarizing linear regressions – simulation of non-linear
predictions – predictive simulation for generalized linear models – fake-data simulation –
simulating and comparing to actual data – predictive simulation to check the fit of a time-series
model – causal inference – randomized experiments – observational studies – causal inference using advanced models – matching – instrumental variables

UNIT IV MULTILEVEL REGRESSION
Multilevel structures – clustered data – multilevel linear models – partial pooling – group-level
predictors – model building and statistical significance – varying intercepts and slopes – scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression – multi-level generalized linear models

UNIT V DATA COLLECTION AND MODEL UNDERSTANDING
Design of data collection – classical power calculations – multilevel power calculations – power
calculation using fake-data simulation – understanding and summarizing fitted models –
uncertainty and variability – variances – R2 and explained variance – multiple comparisons and
statistical significance – analysis of variance – ANOVA and multilevel linear and general linear
models – missing data imputation

TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
 Build and apply linear regression models
 Build and apply logistic regression models
 Build and apply generalized linear models
 Perform simulation using regression models
 Perform casual inference from data
 Build and apply multilevel regression models
 Perform data collection and variance analysis

REFERENCES:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and
multilevel/Hierarchical Models", Cambridge University Press, 2006.
2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
4. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second Edition,
Oxford University Press, 2006.
5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and data
mining applications", Academic Press, 2009.
6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013.
7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An Example- based Approach", Third Edition, Cambridge University Press, 2010.
8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer, 2011