Python for Statistical Analysis

欢迎使用Python进行统计分析！

本课程旨在通过深入统计和数据科学的现实世界来为您定位，以取得成功。

通过实际示例进行学习：我们不会花大量时间学习理论内容并努力将其与实际问题联系起来，而是将重点完全放在应用统计上。学习理论并立即通过Python将其应用到常见问题上，从而为您提供出色的知识和技能。

以演讲为中心的结果：整理数字很容易，并且很快成为计算机而不是人员的领域。人们具有解释和可视化结果的技能，因此我们将重点放在此上，将可视化输出和图形化探索集成到我们的工作流中。此外，还提供了额外的奖励内容，以丰富的方式为报告，文章和演示文稿增添视觉效果，从而使您在人群中脱颖而出。

HIGHEST RATED | Created by Samuel Hinton, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team | Video: h264, 1280×720 | Audio: AAC 48KHz 2ch | Duration: 08:36 H/M | Lec: 53 | 2.76 GB | Language: English | Sub: English [Auto-generated]

Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries

What you’ll learn
Gain deeper insights into data
Use Python to solve common and complex statistical and Machine Learning-related projects
How to interpret and visualize outcomes, integrating visual output and graphical exploration
Learn hypothesis testing and how to efficiently implement tests in Python

Requirements
Python basics

Description
Welcome to Python for Statistical Analysis!

This course is designed to position you for success by diving into the real-world of statistics and data science.

Learn through real-world examples: Instead of sitting through hours of theoretical content and struggling to connect it to real-world problems, we’ll focus entirely upon applied statistics. Taking theory and immediately applying it through Python onto common problems to give you the knowledge and skills you need to excel.

Presentation-focused outcomes: Crunching the numbers is easy, and quickly becoming the domain of computers and not people. The skills people have are interpreting and visualising outcomes and so we focus heavily on this, integrating visual output and graphical exploration in our workflows. Plus, extra bonus content on great ways to spice up visuals for reports, articles and presentations, so that you can stand out from the crowd.

Modern tools and workflows: This isn’t school, where we want to spend hours grinding through problems by hand for reinforcement learning. No, we’ll solve our problems using state-of-the-art techniques and code libraries, utilising features from the very latest software releases to make us as productive and efficient as possible. Don’t reinvent the wheel when the industry has moved to rockets.

Who this course is for?
Data Scientists who want to add to their skillset statistical analysis
Data Scientists who want to do machine learning but want some more statistical foundations before jumping in
Students wanting to learn applied statistics for research, coursework or business