Учебные материалы по анализу данных

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.

Understand the basic concepts of hierarchical clustering, how it works, and how to implement it in Python.

Learn how to remove extra rows quickly using sorting, formulas, and VBA, depending on what works best for you.

Learn how to use the ROUND() function and its alternatives in Excel. Learn how to round decimals, integers, and time values.

Learn how to build and enhance pie charts using Python’s Matplotlib and Pandas libraries. Discover practical code examples and essential design tips to create clear, readable visuals.

Learn to use Excel to calculate compound interest along with practical techniques and real-world applications to enhance your financial analysis skills.

Explore the fundamentals of the Pareto distribution and its applications. Learn how this statistical concept can be applied in various fields. Discover practical examples and visual aids to enhance your understanding.

Learn two reliable methods for applying PivotTable conditional formatting and avoid common pitfalls.

Discover why Euler’s number is everywhere—from banking and biology to machine learning and meteorology—and how this constant powers continuous growth and change.

Visualize inequality at a glance using the Lorenz curve. Discover how to create and interpret Lorenz curves using Python to analyze inequality across income, crime, and other real-world datasets.

Learn how to create, format, and customize Excel pie charts. Discover Excel pie chart options like doughnut charts, pie of pie charts, and exploded pie charts.

The central limit theorem states that, with a sufficiently large sample size, the sampling distribution of the mean will be normally distributed, regardless of the population’s distribution. Learn its formula, key conditions, and applications in statistics and machine learning.