GATE Data Science & AI

Probability and Statistics for GATE DA 2026

Master Probability and Statistics for GATE DA 2026

Looking to crack GATE Data Science & AI 2026? This comprehensive course on Probability and Statistics is tailored exactly as …

101+

Lessons

32+

Hours

MindSpan Education - GATE instructor
MindSpan Education

GATE Expert & Instructor

₹399

₹665 40% OFF

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This Course Includes:
  • 32+ hours of on-demand video
  • Practice Problems
  • Access Until the Next GATE Exam
  • Access on mobile and desktop
  • Chapter-wise Quiz

About This Course

Master Probability and Statistics for GATE DA 2026

Looking to crack GATE Data Science & AI 2026? This comprehensive course on Probability and Statistics is tailored exactly as per the official GATE DA syllabus, helping you build the mathematical confidence needed to solve advanced data-driven problems.

Why This Course?

Whether you're starting from scratch or revising concepts, this course is structured to strengthen your understanding with in-depth theory, intuitive examples, and practice problems modeled on GATE-level difficulty.

What You Will Learn:

  • Counting and Probability: permutations, combinations, sample space, independent/mutually exclusive events, and Bayes’ Theorem
  • Random Variables: discrete and continuous types, PMF, PDF, CDF
  • Distributions: Bernoulli, Binomial, Uniform, Exponential, Poisson, Normal, t-distribution, Chi-squared
  • Statistical Measures: mean, median, mode, variance, standard deviation, correlation, covariance
  • Advanced Concepts: conditional expectation, CLT (Central Limit Theorem), conditional PDF
  • Hypothesis Testing: z-test, t-test, chi-squared test, confidence intervals

Course Highlights:

  • Fully aligned with GATE DA 2026 syllabus
  • Step-by-step video tutorials and concept deep-dives
  • Topic-wise tests and practice quizzes
  • notes and doubt resolution

Join thousands of GATE aspirants preparing smarter with Mindspan Education.

Enroll now for the GATE DA 2026 Test Series — Offer ends on 31st July.

What You'll Learn

  • Understand key Data Science concepts in the GATE syllabus
  • Master mathematical foundations required for AI algorithms
  • Learn to solve complex problems with efficient algorithms
  • Practice with real GATE exam questions and solutions

Requirements

  • Basic understanding of programming concepts
  • Familiarity with fundamental mathematics
  • Enthusiasm to learn and practice regularly

Key Features

Expert Instruction

Learn from experts dedicated to GATE preparation.

Hands-on Projects

Apply what you learn with practical exercises and real-world examples

Practice Tests

Reinforce your knowledge with quizzes and assessments after each module

Course Curriculum

  • Probability and statistics for GATE DS and AI |Course Ove...
  • 1. Set[Review] | Probability & Statistics for GATE DS & A...

  • Probability and statistics for GATE DS and AI |Course Ove...
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  • 1. Set[Review] | Probability & Statistics for GATE DS & A...
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  • 2. Set[Review] |Probability & Statistics for GATE DS & AI...
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  • 3.Sample Space|Probability & Statistics for GATE DS & AI|...
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  • 4.Sample Space|Probability & Statistics for GATE DS & AI|...
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  • 5.Probability Axioms |Probability & Statistics for GATE D...
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  • 6. Probability Axioms |Probability & Statistics for GATE ...
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  • 7.Probability Axioms |Probability & Statistics for GATE D...
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  • 8.Example Discrete Sample space Probability Calculation ...
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  • 9.Example : Continuous Sample space Probability Calculati...
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  • 10. Example : Discrete Infinite Sample space Probability ...
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  • 11. Conditional Probability |Abhinandan kumar #gateda #g...
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  • 12. Example on Conditional Probability |Abhinandan kumar...
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  • 13. Conditional Probability Axioms |Abhinandan kumar #ga...
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  • 14. The Multiplication Rule |Abhinandan kumar #gateda #...
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  • 15.Total probability theorem |Gate datascience and ai pro...
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  • 16.Example : Multiplication rule , Total probability theo...
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  • 17. Bayes’ Theorem |Probability for Gate data science and...
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  • 18. Example : Bayes’ Theorem |Probability for Gate data s...
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  • 19. Example |Probability for Gate data science and ai |Ab...
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  • 20 . Independence of two event |Probability for Gate data...
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  • 21. Independence of event complements |Probability for Ga...
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  • 22. Examples: Independent event |Probability for Gate dat...
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  • 23. Conditional Independence .Probability for Gate data s...
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  • 24. Example: Conditional Independence .Probability for Ga...
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  • 25.Independence of multiple Events |Probability for Gate ...
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  • 26.Example : Independence of multiple Events |Probability...
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  • 27. Counting |Probability for Gate data science and ai | ...
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  • 28. Permutation |Probability for Gate data science and ai...
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  • 29. Combination|Probability for Gate data science and ai ...
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  • 30.Binomial Probability |Probability for Gate data scienc...
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  • 31.Binomial Probability (problems)|Probability for Gate d...
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  • 32.Partition |Probability for Gate data science and ai | ...
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  • 33.Multinomial probability |Probability for Gate data sci...
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  • 34. The random variable |Probability for Gate data scienc...
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  • 35. The random variable |Probability for Gate data scienc...
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  • 36. PMF |Probability for Gate data science and ai |BY AB...
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  • 37. PMF Example |Probability for Gate data science and ai...
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  • 38.Bernoulli Random variable |Probability for Gate data s...
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  • 39. Discrete uniform Random variable |Probability for Gat...
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  • 40. Binomial Random variable |Probability for Gate data s...
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  • 41.Geometric Random variable |Probability for Gate data s...
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  • 42.Expected value of Random variable |Probability for Gat...
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  • 43.Expectation of Random variable |Probability for Gate d...
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  • 44.Elementary properties of Expectation |Probability for ...
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  • 45.The Expected value rule |Probability for Gate data sci...
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  • 46.The Linearity of Expectation |Probability for Gate dat...
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  • 47. Variance |Probability for Gate data science and ai |#...
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  • 48. Properties of Variance |Probability for Gate data sci...
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  • 49. Variance of Bernoulli , uniform |Probability for Gate...
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  • 50.Conditional PMF, Conditional Expectations and variance...
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  • 51.Total Expectations Theorem | Probability for Gate data...
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  • 52.Expectations of Geometric Random variable | #mindspane...
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  • 53. Joint PMF | part 1 | GATE DA | MindSpan Education #pr...
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  • 54: Joint PMF | Part 2 | GATE DA
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  • 55. Marginal PMF | GATE DA | MINDSPAN EDUCATION
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  • 56. Linearity of Expectations | GATE DA | MINDSPAN EDUCATION
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  • 57. Conditional PMF | GATE DA | MindSpan Education
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  • 58.Multiplication Rule (Multiple Random Variable) |GATE D...
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  • 59. Conditional Expectations | Multiple Random Variables ...
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  • 60.TOTAL PROBABILITY Theorem | TOTAL EXPECTATION THEOREM ...
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  • 61. Independence of Random variables | Probability and st...
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  • 62. EXAMPLE : Independence, Conditional independence of R...
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  • 63.Independence and Expectation | Gate DA Probability and...
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  • 64.Independence and Variances | probability for gate da
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  • 65. EXAMPLE: Joint pmf, marginal pmf , conditional pmf , ...
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  • 66. Example: The Hat Problem | Probability and statistics...
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  • 67. Continuous Random Variable | probability density func...
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  • 68. Uniform Probability density function.Probability and ...
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  • 69. Expectation and Variance of Continuous Random variable
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  • 70. Expectation and Variance of Continuous Uniform Random...
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  • 71. Exponential Random variable
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  • 72. Cumulative Distribution Function
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  • 73. Normal(Gaussian) Random Variable
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  • 74. Normal (Gaussian) Random Variable
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  • 75. Examples: PDF, CDF | GATE DA | MINDSPAN EDUCATION
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  • 76. Conditional probability density function | Gate DA | ...
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  • 77. Example : Conditional PDF | Gate DA | MindSpan Education
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  • 78. Memorylessness of Exponential PDF
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  • 79. Total Probability and Total Expectation|Probability f...
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  • 80. Mixed Random variable
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  • 81. Joint PDF
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  • 82. Marginal PDF
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  • 83. JOINT CDF
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  • 84. CONDITIONAL PDF, TOTAL PROBABILITY & EXPECTATION, IND...
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  • 85. EXAMPLE ON JOINT PDF, MARGINAL PDF, CONDITIONAL EXPEC...
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  • 86. Independent Normal Random Variable
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  • 87. Bayes Rule Variation
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  • 88. Bayes Rule Variation
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  • 89. Derived Distribution
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  • 90. Derived Distribution
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  • 91. The distribution of Sum of the independent Random var...
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  • 92. Covariance of Two Random variable
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  • 93. Correlation Coefficient | Gate Da
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  • 94. Problems on COVARIANCE AND CORRELATION | Gate Da
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  • 95. Law of total Expectations, Law of total Variance
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  • 96. Expectation & Variance of Sum of Random Number of Ind...
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  • 97. Central Limit Theorem
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  • 98. Problems on Central Limit Theorem
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