Complete Probability and Statistics for GATE Data Science & AI | GATE DA 2026

About This Course

This comprehensive course is designed for aspirants preparing for GATE DA 2026, covering all essential concepts in Probability and Statistics as per the official syllabus. Whether you're starting from scratch or revising key topics, this course builds a solid mathematical foundation for GATE Data Science & AI.

Topics Covered:

• Counting principles: permutations and combinations
• Probability axioms, sample space, and events
• Independent and mutually exclusive events
• Marginal, conditional, and joint probabilities
• Bayes Theorem
• Conditional expectation and variance
• Descriptive statistics: mean, median, mode, and standard deviation
• Correlation and covariance
• Random variables – discrete and continuous
• Probability mass functions (PMFs) and probability distribution functions (PDFs)
• Important distributions:
  • Discrete: Bernoulli, Binomial, Uniform
  • Continuous: Uniform, Exponential, Poisson, Normal, Standard Normal
  • Others: t-distribution, Chi-squared distribution
• Cumulative distribution function (CDF)
• Conditional PDF
• Central Limit Theorem (CLT)
• Confidence intervals and hypothesis testing: z-test, t-test, chi-squared test

Every concept is explained in a beginner-friendly way with solved examples and practice problems — making it perfect for GATE DA aspirants looking to strengthen their probability and statistics fundamentals.

Start learning now on and access detailed practice material, notes, and quizzes on our learning platform.