Introduction To Statistics In Python

Statistics and Probability with Python libraries: distributions, normal distribution, central limit theorem, correlation

What you'll learn

Understand key statistical concepts using Python.

Analyze datasets with Python libraries like Mathlotlib, Scipy, Binom and many more

Visualize data effectively with Seaborn.

Explore real-world examples of statistics in action.

Requirements

Basic Python knowledge is helpful but not required.

No prior statistics experience needed. You will learn step by step.

Description

Statistics is the science of collecting, analyzing, and drawing meaningful conclusions from data. It’s a powerful tool that helps you make informed predictions and answer a wide range of questions. How likely is someone to buy your product? How many support calls will your team receive? How many jeans should you produce to fit 95% of the population?In this course, you’ll learn to tackle questions like these while building your statistical skills. You’ll explore how to calculate averages, create scatterplots to visualize relationships between numeric values, and measure correlation. You’ll also dive into probability—the foundation of statistical reasoning—and discover how to use Python to design studies, analyze data, and draw reliable conclusions.Beyond these core concepts, the course will guide you through more advanced topics, such as probability distributions, including binomial, Poisson, and normal distributions. You’ll learn how the central limit theorem works, how to generate and interpret sampling distributions, and how to apply transformations to improve data analysis. We will also explore correlation versus causation, confounding variables, and best practices for experimental and observational study design. By the end of the course, you’ll not only be able to summarize and visualize data but also make data-driven decisions and confidently apply statistical reasoning in real-world scenarios using Python and its powerful libraries.Some parts of this course, including the narration, were generated using artificial intelligence. This helps ensure clarity and proper English, as the author’s spoken English may not be fully fluent.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Understanding Your Data: Mean, Median & More

Lecture 2 Introduction to Statistics: Key Concepts

Lecture 3 Understanding Data Spread: Variance & Standard Deviation

Section 3: Random Numbers & Probability in Action

Lecture 4 What Are the Chances?

Lecture 5 Exploring Probability Distributions

Lecture 6 Continuous Probability Distributions

Lecture 7 The Binomial Distribution: Modeling Successes

Section 4: Exploring Key Distributions & the Central Limit Theorem

Lecture 8 Understanding the Normal Distribution

Lecture 9 The Central Limit Theorem in Action

Lecture 10 Understanding the Poisson Distribution

Lecture 11 Exploring Exponential, t-, and Log-Normal Distributions

Section 5: Understanding Correlation and Experimental Design

Lecture 12 Exploring Correlation and Experimental Design

Lecture 13 Correlation: Insights, Transformations, and Cautions

Lecture 14 Understanding Study Design and Its Impact on Data

Lecture 15 Course Completion: Your Statistical Journey

Beginners in Python who want to learn statistics.,Data enthusiasts looking to analyze and visualize data.,Students or professionals seeking practical statistics skills.,Anyone curious about applying Python to real-world data.