Syllabus - Probability and Statistics for Data Science (AD302)

Data Science: Introduction, Data Science Life Cycle

Statistics: Descriptive and Inferential Statistics, Measures of central tendency: Arithmetic Mean, Median and Mode. Geometric mean, Harmonic Mean and Partition values. Measures of dispersion: Dispersion, Range, Quartile Deviation, Mean deviation, Standard Deviation, Variance and Coefficient of Dispersion.

Skewness, Kurtosis, Moments, Measure of skewness and kurtosis.

Theory of probability: Introduction and definition of Probability, Event, Sample Space, Law of addition and multiplication of Probabilities and Conditional Probability. Independent and Dependent events, Bayes’ theorem, Mathematical Expectations and Moment generating functions.

Theoretical Distribution: Discrete Distribution- Binomial Distribution and Poisson Distribution. Continuous Distribution –Rectangular and Normal distribution.

Curve fitting: Curve fitting and Methods of Least square, fitting a Straight line and a Parabola.

Correlation and Regression: Correlation, Coefficient of Correlation, Rank Correlation, Lines of Regression. Multiple and Partial Correlation.

Testing of hypothesis: Null and Alternative hypothesis, two types of errors, level of significance and power of the test.

Tests of significance: Chi-square distribution, test of popular variance and test of goodness of fit. t, F ,Z distribution and tests based on them.