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30 Αυγ 2023 · The Python programming language offers a built-in module called statistics that provides a wide range of functions to perform statistical calculations on data sets. In this tutorial, we will explore the statistics module in detail, discussing its various functions and providing practical examples to help you understand how to use them effectively.
In this step-by-step tutorial, you'll learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, pandas, Matplotlib, and the built-in Python statistics library.
17 Μαΐ 2021 · For an introduction to statistics, this tutorial with real-life examples is the way to go. The notebooks of this tutorial will introduce you to concepts like mean, median, standard deviation, and the basics of topics such as hypothesis testing and probability distributions.
2 Αυγ 2024 · In this tutorial, we will learn about solving statistical problems with Python and will also learn the concept behind it. Let’s start by understanding some concepts that will be useful throughout the article. Note: We will be covering descriptive statistics with the help of the statistics module provided by Python.
22 Αυγ 2024 · Statsmodels is a popular Python library for estimating and testing statistical models, such as linear regression, GLM, and time series models. It is a library that provides classes and functions for the estimation of statistical models, performing hypothesis tests, and conducting data exploration.
19 Αυγ 2022 · Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.
Statistics with Python. This one day course introduces basic statistical concepts used in Data Science with Python. It is more "how do I use this concept in Python" than "what is this concept". Some familiarity with statistical concepts are assumed. Learning Objectives. Participants can/do. understand probability and know typical distributions.
