Python for Statistical Analysis (Udemy.com)

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

( 226 Reviews )

Created by: Samuel Hinton

Produced in 2021

What you will 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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 90 / 100

Course 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 analysisData Scientists who want to do machine learning but want some more statistical foundations before jumping inStudents wanting to learn applied statistics for research, coursework or business

Time: 8.5h

Instructor Details

Samuel Hinton

4.5 Rating
226 Reviews

Hi, I'm Sam and I'm an astrophysicist, data scientist, robotics and software engineer, astronomer and public presenter.

My primary work involves investigating the nature of dark energy, however I also spend a lot of time advocating of open-source development and proper coding practices.With years of experience from the financial software industry to machine learning pipelines classifying objects in the night sky, and teaching experience in statistics, software engineering, data manipulation, computational physics, and much more, I'm dedicated to increasing the level of coding proficiency in the scientific fields, and bringing basic coding knowledge to any eager student.

On top of my research work, I've run national coding workshops with content ranging from complete novices up to research experts. I'm excited to bring my knowledge and content to a wider audience, and hope that my direct and to-the-point teaching attitude allows students to understand the core concepts faster and better, saving students time and stress!

Reviews

4.5

226 total reviews

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Write a Review

By Philip Buhr on 11/21/2020

The Presenter has an infectous passion for statistics and realy got me exited to lean and use this to analyze my own data

By Caio Luna Ferreira on 11/21/2020

As a beginner, It would require much more exercises to absorb and put all this knowledge into use

By JanOve Knutsen on 11/7/2020

Good so far

By Jammie Monclova on 11/7/2020

It is a little early, but I have liked everything so far.

By William Windsor on 11/7/2020

Excellent content, and the instructor Samuel Hinton is knowledgeable, clear, and systematic in his teaching for us students. Highly recommended.

By Sai charan pallati on 11/7/2020

Explanation of topics are not clear, Instructor has to explain things much detail. It is totally a time waste course

By Amidou N'Diaye on 11/1/2020

Samuel is knowledgeable, passionate about the topic (Statistics) and the explanation is crystal clear. Lots of practical examples to work with and build a solid foundation of applied statistics.

By Manish Choudhary on 10/24/2020

amazing journey ?

By John D Calandra on 10/19/2020

I am not that far into the course but I have been an adjunct IT instructor for decades. Based on my experience as an instructor, I can say that Samuel Hinton has a smooth, entertaining delivery that helps me take this course. He is obviously very knowledgeable about the subject matter. I took this course because I dropped out of a similar edX course in which the lectures were long, boring, too much math, and not well explained -- it seemed the instructors went out of their way to make the course too difficult. Thanks Sam, you are providing a course that I can learn from and complete!

By Terry B on 10/15/2020

Le cours commence trs bien, jespre apprendre des choses intressantes pour le data preprocessing en ML

By Jennifer Yen on 10/8/2020

Lots of practical examples.

By Mindaugas Zilionis on 10/7/2020

Everything looks good and interesting but in most of the cases there is a very quick explanation, with a few details how to write a code and with only one or two clear practical examples so far. It seems that professor wants to show how distribution looks. Also, there is no clear references where a student should see in order to understand concepts better.