{"id":1426,"date":"2025-02-22T10:26:37","date_gmt":"2025-02-22T04:56:37","guid":{"rendered":"https:\/\/www.techandtrain.com\/blog\/?p=1426"},"modified":"2025-10-22T14:50:32","modified_gmt":"2025-10-22T09:20:32","slug":"top-100-mathematics-keywords-for-data-science-part-1","status":"publish","type":"post","link":"https:\/\/www.techandtrain.com\/blog\/2025\/02\/top-100-mathematics-keywords-for-data-science-part-1\/","title":{"rendered":"Top 100 mathematics keywords for Data Science – Part 1"},"content":{"rendered":"\n

Whoever is teaching you data science without teaching you Mathematics especially optimization is not teaching it right to you. That’s my biggest learning from Master of Data Science at IIT Gandhinagar – it will take you good 2 years to learn the related mathematics in all four major areas below. It’s not possible to learn this mathematics in few weeks even months, it will take a year or two. Here are the top 100 mathematical keywords<\/strong> commonly used in Data Science, Machine Learning, and AI (sourced from ChatGPT)<\/strong>:<\/p>\n\n\n\n

\n\n\n\n

1. Probability & Statistics<\/h3>\n\n\n\n
    \n
  1. Probability<\/li>\n\n\n\n
  2. Random Variable<\/li>\n\n\n\n
  3. Expectation (Mean)<\/li>\n\n\n\n
  4. Variance<\/li>\n\n\n\n
  5. Standard Deviation<\/li>\n\n\n\n
  6. Skewness<\/li>\n\n\n\n
  7. Kurtosis<\/li>\n\n\n\n
  8. Probability Density Function (PDF)<\/li>\n\n\n\n
  9. Cumulative Distribution Function (CDF)<\/li>\n\n\n\n
  10. Bayes’ Theorem<\/li>\n\n\n\n
  11. Conditional Probability<\/li>\n\n\n\n
  12. Joint Probability<\/li>\n\n\n\n
  13. Likelihood<\/li>\n\n\n\n
  14. Maximum Likelihood Estimation (MLE)<\/li>\n\n\n\n
  15. Prior Probability<\/li>\n\n\n\n
  16. Posterior Probability<\/li>\n\n\n\n
  17. Hypothesis Testing<\/li>\n\n\n\n
  18. Null Hypothesis (H0H_0)<\/li>\n\n\n\n
  19. Alternative Hypothesis (HAH_A)<\/li>\n\n\n\n
  20. p-value<\/li>\n\n\n\n
  21. Confidence Interval<\/li>\n\n\n\n
  22. T-test<\/li>\n\n\n\n
  23. Chi-square Test<\/li>\n\n\n\n
  24. ANOVA (Analysis of Variance)<\/li>\n\n\n\n
  25. Z-score<\/li>\n\n\n\n
  26. Central Limit Theorem (CLT)<\/li>\n\n\n\n
  27. Law of Large Numbers<\/li>\n\n\n\n
  28. Binomial Distribution<\/li>\n\n\n\n
  29. Poisson Distribution<\/li>\n\n\n\n
  30. Normal Distribution<\/li>\n\n\n\n
  31. Gaussian Distribution<\/li>\n\n\n\n
  32. Exponential Distribution<\/li>\n\n\n\n
  33. Log-normal Distribution<\/li>\n<\/ol>\n\n\n\n
    \n\n\n\n

    2. Linear Algebra<\/h3>\n\n\n\n
      \n
    1. Vector<\/li>\n\n\n\n
    2. Matrix<\/li>\n\n\n\n
    3. Scalar<\/li>\n\n\n\n
    4. Tensor<\/li>\n\n\n\n
    5. Eigenvalues<\/li>\n\n\n\n
    6. Eigenvectors<\/li>\n\n\n\n
    7. Determinant<\/li>\n\n\n\n
    8. Singular Value Decomposition (SVD)<\/li>\n\n\n\n
    9. Principal Component Analysis (PCA)<\/li>\n\n\n\n
    10. Covariance Matrix<\/li>\n\n\n\n
    11. Orthogonality<\/li>\n\n\n\n
    12. Dot Product<\/li>\n\n\n\n
    13. Cross Product<\/li>\n\n\n\n
    14. Matrix Multiplication<\/li>\n\n\n\n
    15. Rank of a Matrix<\/li>\n\n\n\n
    16. Trace of a Matrix<\/li>\n\n\n\n
    17. Identity Matrix<\/li>\n\n\n\n
    18. Inverse Matrix<\/li>\n\n\n\n
    19. Transpose of a Matrix<\/li>\n\n\n\n
    20. Diagonalization<\/li>\n\n\n\n
    21. Gram-Schmidt Process<\/li>\n<\/ol>\n\n\n\n
      \n\n\n\n

      3. Calculus & Optimization<\/h3>\n\n\n\n
        \n
      1. Derivative<\/li>\n\n\n\n
      2. Partial Derivative<\/li>\n\n\n\n
      3. Gradient<\/li>\n\n\n\n
      4. Hessian Matrix<\/li>\n\n\n\n
      5. Jacobian Matrix<\/li>\n\n\n\n
      6. Chain Rule<\/li>\n\n\n\n
      7. Gradient Descent<\/li>\n\n\n\n
      8. Stochastic Gradient Descent (SGD)<\/li>\n\n\n\n
      9. Learning Rate<\/li>\n\n\n\n
      10. Loss Function<\/li>\n\n\n\n
      11. Cost Function<\/li>\n\n\n\n
      12. Objective Function<\/li>\n\n\n\n
      13. Convex Function<\/li>\n\n\n\n
      14. Concave Function<\/li>\n\n\n\n
      15. Local Minimum<\/li>\n\n\n\n
      16. Global Minimum<\/li>\n\n\n\n
      17. Local Maximum<\/li>\n\n\n\n
      18. Global Maximum<\/li>\n\n\n\n
      19. Lagrange Multipliers<\/li>\n\n\n\n
      20. Optimization<\/li>\n\n\n\n
      21. Regularization<\/li>\n\n\n\n
      22. L1 Regularization (Lasso)<\/li>\n\n\n\n
      23. L2 Regularization (Ridge)<\/li>\n<\/ol>\n\n\n\n
        \n\n\n\n

        4. Machine Learning Metrics & Functions<\/h3>\n\n\n\n
          \n
        1. Accuracy<\/li>\n\n\n\n
        2. Precision<\/li>\n\n\n\n
        3. Recall<\/li>\n\n\n\n
        4. F1-score<\/li>\n\n\n\n
        5. ROC Curve<\/li>\n\n\n\n
        6. AUC (Area Under Curve)<\/li>\n\n\n\n
        7. Confusion Matrix<\/li>\n\n\n\n
        8. True Positive (TP)<\/li>\n\n\n\n
        9. True Negative (TN)<\/li>\n\n\n\n
        10. False Positive (FP)<\/li>\n\n\n\n
        11. False Negative (FN)<\/li>\n\n\n\n
        12. Logarithm (Log)<\/li>\n\n\n\n
        13. Exponential Function<\/li>\n\n\n\n
        14. Softmax Function<\/li>\n\n\n\n
        15. Sigmoid Function<\/li>\n\n\n\n
        16. Activation Function<\/li>\n\n\n\n
        17. Cross-Entropy Loss<\/li>\n\n\n\n
        18. Mean Squared Error (MSE)<\/li>\n\n\n\n
        19. Mean Absolute Error (MAE)<\/li>\n\n\n\n
        20. Hinge Loss<\/li>\n\n\n\n
        21. Kullback-Leibler Divergence<\/li>\n\n\n\n
        22. Entropy<\/li>\n\n\n\n
        23. Information Gain<\/li>\n<\/ol>\n\n\n\n
          \n\n\n\n

          These 100 mathematical keywords<\/strong> form the foundation of Data Science, Machine Learning, and AI<\/strong>.<\/p>\n\n\n\n