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Data Science Tutorials
Advance your data career with our data science tutorials. We walk you through challenging data science functions and models step-by-step.
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Probability Mass Function: A Guide to Discrete Distributions
Learn how the probability mass function defines discrete probability distributions. Explore its properties, examples, and differences from probability density functions.
Vidhi Chugh
June 20, 2025
Hessian Matrix: A Guide to Second-Order Derivatives in Optimization and Beyond
Understand the role of the Hessian matrix in multivariable calculus and optimization. Learn how it’s used to analyze curvature, locate critical points, and guide algorithms in machine learning.
Vidhi Chugh
June 16, 2025
Orthogonal Matrix: An Explanation with Examples and Code
Learn about orthogonal matrices with practical examples and real-world applications in linear algebra and data science.
Arunn Thevapalan
June 12, 2025
Least Squares Method: How to Find the Best Fit Line
Use this method to make better predictions from real-world data. Learn how to minimize errors and find the most reliable trend line.
Amberle McKee
June 12, 2025
The T-Distribution: A Key Tool for Small Sample Inference
Understand how the t-distribution helps when sample sizes are small or population variance is unknown. Compare it to the normal and Z-distributions to learn when each is appropriate.
Vidhi Chugh
June 11, 2025
Law of Large Numbers: A Key to Understanding Uncertainty
Understand how the law of large numbers supports statistical reasoning. See how it ensures consistency in averages over time and powers predictions in fields like sports, finance, and science.
Laiba Siddiqui
June 5, 2025
Introduction to the WHERE Clause in SQL
In this tutorial, you will be introduced to filtering rows in SQL using the WHERE clause.
Sayak Paul
May 29, 2025
The Sigmoid Function: A Key Component in Data Science
Explore the significance of the sigmoid function in neural networks and logistic regression, with practical insights for data science applications.
Vikash Singh
May 28, 2025
Triangle Inequality: From Geometry to Machine Learning
The triangle inequality is more than just a geometric principle - it's a cornerstone of distance measures in data science. Discover how this elegant mathematical concept powers algorithms across computational geometry, optimization, and machine learning.
Vinod Chugani
May 27, 2025
R-Squared Explained: How Well Does Your Regression Model Fit?
Learn what R-squared means in regression analysis, how to calculate it, and when to use it to evaluate model performance. Compare it to related metrics with examples in R and Python.
Elena Kosourova
May 14, 2025
Time Series Decomposition: Understand Trends, Seasonality, and Noise
Learn how to break down time series data into meaningful components like trend, seasonality, and residuals using additive and multiplicative models. Learn about both classical and STL methods. Explore seasonal adjustments and practice forecasting with decomposition models.
Josef Waples
Laiba Siddiqui
April 30, 2025
Gaussian Elimination: A Method to Solve Systems of Equations
Learn the Gaussian elimination algorithm through step-by-step examples, code implementations, and practical applications in data science.
Arunn Thevapalan
April 18, 2025