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SYLLABUS

UNIT-I

Descriptive Statistics and Methods for Data Science Data Science, Statistics Introduction, Population vs Sample, Collection of Data, Primary and Secondary Data, Type of Variable: Dependent and Independent, Categorical and Continuous Variables. Data Visualization, Measures of Central Tendency, Measures of Variability (Spread or Variance), Skewness, Kurtosis, Correlation, Correlation Coefficient, Rank Correlation, Regression Coefficients, Principle of Least Squares, Method of Least Squares, Regression Lines.

UNIT-II

Probability Probability, Probability Axioms, Addition Law and Multiplicative Law of Probability, Conditional Probability, Baye’s Theorem, Random Variables (Discrete and Continuous), Probability Density Functions, Properties, Mathematical Expectation.

UNIT-III

Probability Distributions Probability Distribution – Binomial, Poisson Approximation to the Binomial Distribution and Normal Distribution – Their Properties.

UNIT-IV

Estimation and Testing of Hypothesis, Large Sample Tests Estimation – Parameters, Statistics, Sampling Distribution, Point Estimation, Formulation of Null Hypothesis, Alternative Hypothesis, The Critical and Acceptance Regions, Level of Significance, Two Types of Errors and Power of the Test. Large Sample Tests: Test for Single Proportion, Difference of Proportions, Test for Single Mean and Difference of Means, Confidence Interval for Parameters in One Sample and Two Sample Problems.

UNIT-V

Small Sample Tests Student t-distribution (Test for Single Mean, Two Means and Paired t-test), Testing of Equality of Variances (F-test), χ2 -test for Goodness of Fit, χ2 -test for Independence of Attributes.