Intro to Probability and Statistics

Channel:
United States Coding Tech
Subscribers:
725,000
Published on ● Video Link: https://www.youtube.com/watch?v=Q4SZdTp7Yis


Duration: 1:30:12
6,792 views
184

This tutorial will offer a quick overview of many of the essentials of statistics used to solve real-world problems. We'll start by looking at how to build a simple hypothesis test based on a practical e-commerce problem. Then we'll see how we can expand on this simple test using one of the most powerful tools in statistics: the linear model. No previous experience with statistics required!

EVENT:

PyData New York 2019

SPEAKER:

Will Kurt

PUBLICATION PERMISSIONS:

PyData provided Coding Tech with the permission to republish PyData talks.

CREDITS:

PyData YouTube channel: https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w



Other Videos By Coding Tech


2019-12-09Building Responsive Layouts
2019-12-08Jupyter Notebooks in Visual Studio Code
2019-12-07Curious Use Cases of GraphQL (and The Future of GraphQL)
2019-12-06What’s New in Material Design For the Web
2019-12-06The Rise of JAMStack
2019-12-06A Crash Course in Applied Linear Algebra || Patrick Landreman
2019-12-05Getting Started with Python in Visual Studio Code
2019-12-04Write Less Code, Generate More
2019-12-04Modern Enterprise Java From The Ground Up
2019-12-03How I Hacked 30 Mobile Banking Apps (And The Future of API Security)
2019-12-03Intro to Probability and Statistics
2019-12-02Building Full-stack C# Web Apps with Blazor in .NET Core 3.0
2019-12-01Joy - Maintaining Passion for Programming
2019-11-30Let's Build A Video Game With Unity and TensorFlow
2019-11-29Faster Apps With JSON.parse
2019-11-29No One Needs A Server - A Fresh Look At Cloud
2019-11-28Cypress.io – The State of the Art End to End Testing Tool
2019-11-27Introduction To TensorFlow 2.0
2019-11-27Machine Learning Magic For Your Web App with TensorFlowJS
2019-11-27Optimizing Docker Builds for Python Applications
2019-11-26Should You Use Kubernetes and Docker In Your Next Project?


Tags:
statistics
probability
intro into probability
data science
data analysis