Data science has a pretty diverse glossary.

I once prepared the following glossary sheet, which lists the 75 most common and important terms data scientists use frequently in their day-to-day work. Thus, being aware of them is extremely crucial.

Wherever possible, I have linked my reference resources that explain these terms in detail.

How many terms do you know?

Letâ€™s discuss them in brief one by one:

A:

**Accuracy**: Measure of the correct predictions divided by the total predictions.**Area Under Curve**: Metric representing the area under the Receiver Operating Characteristic (ROC) curve, used to evaluate classification models.**ARIMA**: Autoregressive Integrated Moving Average, a time series forecasting method.

B:

**Bias**: The difference between the true value and the predicted value in a statistical model.**Bayes Theorem**: Probability formula that calculates the likelihood of an event based on prior knowledge.**Binomial Distribution**: Probability distribution that models the number of successes in a fixed number of independent Bernoulli trials.

C:

**Clustering**: Grouping data points based on similarities. Read the newsletter issue on 6 types of clustering algorithms here: The Categorization of Clustering Algorithms in Machine Learning**Confusion Matrix**: Table used to evaluate the performance of a classification model.**Cross-validation**: Technique to assess model performance by dividing data into subsets for training and testing. Read about 5 must-know cross-validation techniques here.Here are three more terms that come to my mind:

Calibration: A crash course on calibration.

Conformal prediction: Conformal Predictions: Build Confidence in Your ML Model's Predictions

D:

**Decision Trees**: A tree-like model used for classification and regression tasks.**Dimensionality Reduction**: Process of reducing the number of features in a dataset while preserving important information.**Discriminative Models**: Models that learn the boundary between different classes. Read about discriminative models in this issue and how they differ from generative models in this issue.

E:

**Ensemble Learning**: Technique that combines multiple models to improve predictive performance.**EDA (Exploratory Data Analysis)**: Process of analyzing and visualizing data to understand its patterns and properties. Learn about 8 automated EDA tools in this issue.**Entropy**: Measure of uncertainty or randomness in information.

F:

**Feature Engineering**: Process of creating new features from existing data to improve model performance.**F-score**: Metric that balances precision and recall for binary classification.**Feature Extraction**: Process of automatically extracting meaningful features from data.

G:

**Gradient Descent**: Optimization algorithm used to minimize a function by adjusting parameters iteratively.**Gaussian Distribution**: Normal distribution with a bell-shaped probability density function.**Gradient Boosting**: Ensemble learning method that builds multiple weak learners sequentially. Adaboost is a popular gradient-boosting algorithm. Learn how it works in this newsletter issue.**Graphs**is another term that comes to my mind, which we covered here: A Crash Course on Graph Neural Networks (Implementation Included)

H:

**Hypothesis**: Testable statement or assumption in statistical inference.**Hierarchical Clustering**: Clustering method that organizes data into a tree-like structure.**Heteroscedasticity**: Unequal variance of errors in a regression model.

I:

**Information Gain**: Measure used in decision trees to determine the importance of a feature.**Independent Variable**: Variable that is manipulated in an experiment to observe its effect on the dependent variable.**Imbalance**: Situation where the distribution of classes in a dataset is not equal.

J:

**Jupyter**: Interactive computing environment used for data analysis and machine learning.**Joint Probability**: Probability of two or more events occurring together.**Jaccard Index**: Measure of similarity between two sets.

K:

**Kernel Density Estimation**: Non-parametric method to estimate the probability density function of a continuous random variable.**KS Test (Kolmogorov-Smirnov Test)**: Non-parametric test to compare two probability distributions. Read about it in this newsletter issue.**KMeans Clustering**: Partitioning data into K clusters based on similarity.

L:

**Likelihood**: Chance of observing the data given a specific model. Understand the difference between likelihood and probability in this newsletter issue.**Linear Regression**: Statistical method for modeling the relationship between dependent and independent variables.**L1/L2 Regularization**: Techniques to prevent overfitting by adding penalty terms to the modelâ€™s loss function. Ever wondered where did the regularization term originated from? Read this article to understand everything about it: The Probabilistic Origin of Regularization.

M:

**Maximum Likelihood Estimation**: Method to estimate the parameters of a statistical model. Hereâ€™s an intuitive explanation to maximum likelihood estimation (MLE) in machine learning.**Multicollinearity**: A situation where two or more independent variables are highly correlated in a regression model.**Mutual Information**: Measure of the amount of information shared between two variables.

N:

**Naive Bayes**: Probabilistic classifier based on Bayes Theorem with the assumption of feature independence.**Normalization**: Scaling data to have a mean of 0 and std-dev of 1.**Null Hypothesis**: Hypothesis of no significant difference or effect in statistical testing.

O:

**Overfitting**: When a model performs well on training data but poorly on new, unseen data.**Outliers**: Data points that significantly differ from other data points in a dataset.**One-hot encoding**: Process of converting categorical variables into binary vectors. Hereâ€™s an issue on the most overlooked problem with one-hot encoding.

P:

**PCA (Principal Component Analysis)**: Dimensionality reduction technique to transform data into orthogonal components. I have a full deep dive on PCA, which you can read here: Formulating the Principal Component Analysis (PCA) Algorithm From Scratch.**Precision**: Proportion of true positive predictions among all positive predictions in a classification model. If you struggle to interpret precision and recall, this issue will solve it for you forever.**p-value**: Probability of observing a result at least as extreme as the one obtained if the null hypothesis is true.

Q:

**QQ-plot (Quantile-Quantile Plot)**: Graphical tool to compare the distribution of two datasets. Hereâ€™s a visual and intuitive guide to the QQ plot that will teach you how this plot is created and how to interpret it.**QR decomposition**: Factorization of a matrix into an orthogonal and an upper triangular matrix.**Quantization**: Converting the precision of numbers (mainly parameters) used in our model from a higher precision, like a 32-bit floating point number, to a lower precision, like 4-bit integers. We covered it here: Quantization: Optimize ML Models to Run Them on Tiny Hardware

R:

**Random Forest**: Ensemble learning method that uses multiple decision trees to make predictions with the help of bagging. Understand why bagging is so ridiculously effective at variance reduction here.**Recall**: Proportion of true positive predictions among all actual positive instances in a classification model.**ROC Curve (Receiver Operating Characteristic Curve)**: Graph showing the performance of a binary classifier at different thresholds.

S:

**SVM (Support Vector Machine)**: Supervised machine learning algorithm used for classification and regression.**Standardisation**: Scaling data to have a mean of 0 and a standard deviation of 1.**Sampling**: Process of selecting a subset of data points from a larger dataset.

T:

**t-SNE (t-Distributed Stochastic Neighbor Embedding)**: Dimensionality reduction technique for visualizing high-dimensional data in lower dimensions. I have a full deep dive on t-SNE, which you can read here: Formulating and Implementing the t-SNE Algorithm From Scratch.**t-distribution**: Probability distribution used in hypothesis testing when the sample size is small. The above t-SNE guide will also clear what t-distribution is.**Type I/II Error**: Type I error is a false positive, and Type II error is a false negative in hypothesis testing.

U:

**Underfitting**: When a model is too simple to capture the underlying patterns in the data.**UMAP (Uniform Manifold Approximation and Projection)**: Dimensionality reduction technique for visualizing high-dimensional data.**Uniform Distribution**: Probability distribution where all outcomes are equally likely.

V:

**Variance**: Measure of the spread of data points around the mean.**Validation Curve**: Graph showing how model performance changes with different hyperparameter values.**Vanishing Gradient**: Issue in deep neural networks when gradients become very small during training.

W:

**Word embedding**: Representation of words as dense vectors in natural language processing. If you want to learn about the history of embeddings, you should not miss this insightful issue: A Pivotal Moment in NLP Research Which Made Static Embeddings (Almost) Obsolete.**Word cloud**: Visualization of text data where word frequency is represented through the size of the word.**Weights**: Parameters that are learned by a machine learning model during training.

X:

**XGBoost**: Extreme Gradient Boosting, a popular gradient boosting library. We formulated and implemented XGBoost from scratch here.**XLNet**: Generalized Autoregressive Pretraining of Transformers, a language model.

Y:

**YOLO (You Only Look Once)**: Real-time object detection system.**Yellowbrick**: Python library for machine learning visualization and diagnostic tools.

Z:

**Z-score**: Standardized value representing how many standard deviations a data point is from the mean.**Z-test**: Statistical test used to compare a sample mean to a known population mean.**Zero-shot learning**: Machine learning method where a model can recognize new classes without seeing explicit examples during training.

đŸ‘‰ Over to you: Of course, a lot has been left out here. As an exercise, can you add more terms to this?

**For those who want to build a career in DS/ML on core expertise, not fleeting trends:**

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

Conformal Predictions: Build Confidence in Your ML Modelâ€™s Predictions

Quantization: Optimize ML Models to Run Them on Tiny Hardware

5 Must-Know Ways to Test ML Models in Production (Implementation Included)

Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

And many many more.

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