Fundamentals of Data Science and Analytics for BE Anna University R21CBCS (III- CS & BS - AD3491)  (Paperback, I.A. DHOTRE)

UNIT I INTRODUCTION TO DATA SCIENCE Need for data science - benefits and uses - facets of data - data science process - setting the research goal - retrieving data - cleansing, integrating and transforming data - exploratory data analysis - build the models - presenting and building applications. (Chapter - 1) UNIT II DESCRIPTIVE ANALYTICS Frequency distributions - Outliers - interpreting distributions - graphs - averages - describing variability - interquartile range - variability for qualitative and ranked data - Normal distributions - z scores -correlation - scatter plots - regression - regression line - least squares regression line - standard error of estimate - interpretation of r2 - multiple regression equations - regression toward the mean. (Chapter - 2) UNIT III INFERENTIAL STATISTICS Populations - samples - random sampling - Sampling distribution - standard error of the mean - Hypothesis testing - z-test - z-test procedure -decision rule - calculations - decisions - interpretations - one-tailed and two-tailed tests - Estimation - point estimate - confidence interval - level of confidence - effect of sample size. (Chapter - 3) UNIT IV ANALYSIS OF VARIANCE t-test for one sample - sampling distribution of t - t-test procedure - t-test for two independent samples - p-value - statistical significance - t-test for two related samples. F-test - ANOVA - Two factor experiments - three f-tests - two-factor ANOVA - Introduction to chi-square tests. (Chapter - 4) UNIT V PREDICTIVE ANALYTICS Linear least squares - implementation - goodness of fit - testing a linear model - weighted resampling. Regression using StatsModels - multiple regression - nonlinear relationships - logistic regression - estimating parameters - Time series analysis - moving averages - missing values - serial correlation - autocorrelation. Introduction to survival analysis. (Chapter - 5)