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1. A/B Testing: A statistical method used to compare two versions of a product, webpage, or model to determine which performs better.

2. Accuracy: The measure of how often a classification model correctly predicts outcomes among all instances it evaluates.

3. Adaboost: An ensemble learning algorithm that combines weak classifiers to create a strong classifier.

4. Algorithm: A step-by-step set of instructions or rules followed by a computer to solve a problem or perform a task.

5. Analytics: The process of interpreting and examining data to extract meaningful insights.

6. Anomaly Detection: Identifying unusual patterns or outliers in data.

7. ANOVA (Analysis of Variance): A statistical method used to analyze the differences among group means in a sample.

8. API (Application Programming Interface): A set of rules that allows one software application to interact with another.

9. AUC-ROC (Area Under the ROC Curve): A metric that tells us how well a classification model is doing overall, considering different ways of deciding what counts as a positive or negative prediction.

10. Batch Gradient Descent: An optimization algorithm that updates model parameters using the entire training dataset (different from mini-batch gradient descent)

11. Bayesian Statistics: A statistical approach that combines prior knowledge with observed data.

12. BI (Business Intelligence): Technologies, processes, and tools that help organizations make informed business decisions.

13. Bias: An error in a model that causes it to consistently predict values away from the true values.

14. Bias-Variance Tradeoff: The balance between the error introduced by bias and variance in a model.

15. Big Data: Large and complex datasets that cannot be easily processed using traditional data processing methods.

16. Binary Classification: Categorizing data into two groups, such as spam or not spam.

17. Bootstrap Sampling: A resampling technique where random samples are drawn with replacement from a dataset.

18. Categorical data: variables that represent categories or groups and can take on a limited, fixed number of distinct values.

19. Chi-Square Test: A statistical test used to determine if there is a significant association between two categorical variables.

20. Classification: Categorizing data points into predefined classes or groups.

21. Clustering: Grouping similar data points together based on certain criteria.

22. Confidence Interval: A range of values used to estimate the true value of a parameter with a certain level of confidence.

23. Confusion Matrix: A table used to evaluate the performance of a classification algorithm.

24. Correlation: A statistical measure that describes the degree of association between two variables.

25. Covariance: A measure of how much two random variables change together.

26. Cross-Entropy Loss: A loss function commonly used in classification problems.

27. Cross-Validation: A technique to assess the performance of a model by splitting the data into multiple subsets for training and testing.

28. Data Cleaning: The process of identifying and correcting errors or inconsistencies in datasets.

29. Data Mining: Extracting valuable patterns or information from large datasets.

30. Data Preprocessing: Cleaning and transforming raw data into a format suitable for analysis.

31. Data Visualization: Presenting data in graphical or visual formats to aid understanding.

32. Decision Boundary: The dividing line that separates different classes in a classification problem.

33. Decision Tree: A tree-like model that makes decisions based on a set of rules.

34. Dimensionality Reduction: Reducing the number of features in a dataset while retaining important information.

35. Eigenvalue and Eigenvector: Concepts used in linear algebra, often employed in dimensionality reduction to transform and simplify complex datasets.

36. Elastic Net: A regularization technique that combines L1 and L2 penalties.

37. Ensemble Learning: Combining multiple models to improve overall performance and accuracy.

38. Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand its characteristics and relationships.

39. F1 Score: A metric that combines precision and recall in classification models.

40. False Positive and False Negative: Incorrect predictions in binary classification.

41. Feature: data column thats used as the input for ML models to make predictions.

42. Feature Engineering: Creating new features from existing ones to improve model performance.

43. Feature Extraction: Reducing the dimensionality of data by selecting important features.

44. Feature Importance: Assessing the contribution of each feature to the models predictions.

45. Feature Selection: Choosing the most relevant features for a model.

46. Gaussian Distribution: A type of probability distribution often used in statistical modeling.

47. Geospatial Analysis: Analyzing and interpreting patterns and relationships within geographic data.

48. Gradient Boosting: An ensemble learning technique where weak models are trained sequentially, each correcting the errors of the previous one.

49. Gradient Descent: An optimization algorithm used to minimize the error in a model by adjusting its parameters.

50. Grid Search: A method for tuning hyperparameters by evaluating models at all possible combinations.

51. Heteroscedasticity: Unequal variability of errors in a regression model.

52. Hierarchical Clustering: A method of cluster analysis that organizes data into a tree-like structure of clusters, where each level of the tree shows the relationships and similarities between different groups of data points.

53. Hyperparameter: A parameter whose value is set before the training process begins.

54. Hypothesis Testing: A statistical method to test a hypothesis about a population parameter based on sample data.

55. Imputation: Filling in missing values in a dataset using various techniques.

56. Inferential Statistics: A branch of statistics that involves making inferences about a population based on a sample of data.

57. Information Gain: A measure used in decision trees to assess the effectiveness of a feature in classifying data.

58. Interquartile Range (IQR): A measure of statistical dispersion, representing the range between the first and third quartiles.

59. Joint Plot: A type of data visualization in Seaborn used for exploring relationships between two variables and their individual distributions.

60. Joint Probability: The probability of two or more events happening at the same time, often used in statistical analysis.

61. Jupyter Notebook: An open-source web application for creating and sharing documents containing live code, equations, visualizations, and narrative text.

62. K-Means Clustering: A popular algorithm for partitioning a dataset into distinct, non-overlapping subsets.

63. K-Nearest Neighbors (KNN): A simple and widely used classification algorithm based on how close a new data point is to other data points.

64. L1 Regularization: Adding the absolute values of coefficients as a penalty term to the loss function.

65. L2 Regularization (Ridge): Adding the squared values of coefficients as a penalty term to the loss function.

66. Linear Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables.

67. Log Likelihood: The logarithm of the likelihood function, often used in maximum likelihood estimation.

68. Logistic Function: A sigmoid function used in logistic regression to model the probability of a binary outcome.

69. Logistic Regression: A statistical method for predicting the probability of a binary outcome.

70. Machine Learning: A subset of artificial intelligence that enables systems to learn and make predictions from data.

71. Mean Absolute Error (MAE): A measure of the average absolute differences between predicted and actual values.

72. Mean Squared Error (MSE): A measure of the average squared difference between predicted and actual values.

73. Mean: The average value of a set of numbers.

74. Median: The middle value in a set of sorted numbers.

75. Metrics: Criteria used to assess the performance of a machine learning model, such as accuracy, precision, recall, and F1 score.

76. Model Evaluation: Assessing the performance of a machine learning model using various metrics.

77. Multicollinearity: The presence of a high correlation between independent variables in a regression model.

78. Multi-Label Classification: Assigning multiple labels to an input, as opposed to just one.

79. Multivariate Analysis: Analyzing data with multiple variables to understand relationships between them.

80. Naive Bayes: A probabilistic algorithm based on Bayes theorem used for classification.

81. Normalization: Scaling numerical variables to a standard range.

82. Null Hypothesis: A statistical hypothesis that assumes there is no significant difference between observed and expected results.

83. One-Hot Encoding: A technique to convert categorical variables into a binary matrix for machine learning models.

84. Ordinal Variable: A categorical variable with a meaningful order but not necessarily equal intervals.

85. Outlier: An observation that deviates significantly from other observations in a dataset.

86. Overfitting: A model that performs well on the training data but poorly on new, unseen data.

87. Pandas: A standard data manipulation library for Python for working with structured data.

88. Pearson Correlation Coefficient: A measure of the linear relationship between two variables.

89. Poisson Distribution: A discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space.

90. Precision: The ratio of true positive predictions to the total number of positive predictions made by a classification model.

91. Predictive Analytics: Using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.

92. Principal Component Analysis (PCA): A dimensionality reduction technique that transforms data into a new framework of features, simplifying the information while preserving its fundamental patterns.

93. Principal Component: The axis that captures the most variance in a dataset in principal component analysis.

94. P-value: The probability of obtaining a result as extreme as, or more extreme than, the observed result during hypothesis testing.

95. Q-Q Plot (Quantile-Quantile Plot): A graphical tool to assess if a dataset follows a particular theoretical distribution.

96. Quantile: A data point or set of data points that divide a dataset into equal parts.

97. Random Forest: An ensemble learning method that constructs a multitude of decision trees and merges them together for more accurate and stable predictions.

98. Random Sample: A sample where each member of the population has an equal chance of being selected.

99. Random Variable: A variable whose possible values are outcomes of a random phenomenon.

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130 Data Science Terms Every Data Scientist Should Know | by Anjolaoluwa Ajayi | . | Jan, 2024 - Medium