Data Analysis Tutorials

Examine the mathematical principles behind random walks and explore their forms, from one-dimensional paths to biased and Gaussian models. Use Python to discover how these stochastic processes inform real-world phenomena in biology, physics, and finance.

Explore simple subtraction for quick and effortless date calculations. Apply the DAYS() and DATEDIF() functions to achieve precise date intervals. Use NETWORKDAYS() and TODAY() to track working days and automatically exclude weekends and holidays.

Learn how to multiply in Excel, including how to multiply columns and rows. Learn to use simple formulas as well as functions like PRODUCT() and SUMPRODUCT().

Explore the differences between mean and median, learn their applications in data analysis, and know how to choose the right measure for different scenarios.

Discover the fundamentals of the exponential distribution and its applications in real-world scenarios. Learn how to calculate probabilities and understand its significance in various fields. Explore practical examples and visualizations.

Learn how to keep important rows and columns visible while you scroll in Excel. I’ve explained Freeze Panes step-by-step, with tips for solving everyday problems and improving your workflow.

Discover the importance of multilevel modeling in analyzing hierarchical data structures. Learn how to account for variability within and between groups using fixed and random effects. Apply these concepts to uncover deeper insights in fields like education, healthcare, and social sciences.

Discover how the Fibonacci sequence works. Explore its mathematical properties and real-world applications.

Discover how to master SQL subqueries to enhance your database queries. Learn about correlated, non-correlated, and recursive subqueries. Learn about execution order and how to combine SQL subqueries with other SQL features.

Discover how slicing can effortlessly extract, rearrange, and analyze your data. Harness negative indices, multi-dimensional slices, and advanced step values for full precision.

Discover how propensity scores help minimize confounding in observational research, with detailed applications in real-world studies.

Explore the fundamentals of eigendecomposition and its applications in data science and machine learning.