Probability and Statistics for Data Science
The complete RGPV syllabus for Probability and Statistics for Data Science (AD302), the third-semester course for B.Tech Artificial Intelligence & Data Science under the AICTE Flexible Curricula — descriptive and inferential statistics, probability theory, theoretical distributions, correlation and regression, and hypothesis testing.
Probability & Statistics for Data Science
by Dr. D.C. Agarwal & Dr. Pradeep K. Joshi · ₹440 — covers this full RGPV syllabus.
Course contents — unit by unit
Unit 1 · Data Science & Descriptive Statistics
Data science — introduction and life cycle. Statistics — descriptive vs inferential; measures of central tendency (arithmetic, geometric and harmonic mean, median, mode, partition values); measures of dispersion (range, quartile deviation, mean deviation, standard deviation, variance, coefficient of dispersion).
Unit 2 · Moments & Theory of Probability
Skewness, kurtosis, moments and their measures. Theory of probability — definitions, event, sample space, addition and multiplication laws, conditional probability, independent and dependent events, Bayes' theorem, mathematical expectation and moment-generating functions.
Unit 3 · Theoretical Distributions & Curve Fitting
Discrete distributions (binomial, Poisson); continuous distributions (rectangular, normal). Curve fitting by the method of least squares — fitting a straight line and a parabola.
Unit 4 · Correlation & Regression
Correlation and coefficient of correlation, rank correlation, lines of regression, multiple and partial correlation.
Unit 5 · 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 variance and goodness of fit; t, F and Z distributions and tests based on them.