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30-Day Money-Back Guarantee

This course includes:

  • 8.5 hours on-demand video
  • 3 articles
  • 36 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Statistics

Python for Statistical Analysis

Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries
Rating: 4.7 out of 54.7 (1,752 ratings)
48,880 students
Created by Samuel Hinton, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
Last updated 11/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

6 sections • 56 lectures • 8h 37m total length

  • Preview08:10
  • Preview05:23
  • BONUS: Learning Path
    00:34
  • Live Install and Verification
    04:27
  • Coding Editors
    04:31
  • Live Coding Editor Comparison
    06:04
  • File Management
    02:43

  • Preview07:00
  • Loading Data - Practical Example
    14:42
  • Dataset Preparation - Practical Example
    15:45
  • Dealing with Outliers - Practical Example
    14:09
  • 1D Distribution Overview
    01:07
  • 1D Histograms - Practical Example
    15:57
  • 1D Bee Swarm - Practical Example
    06:44
  • 1D Box and Violin - Practical Example
    09:59
  • 1D Empirical CDF and Pandas Describe - Practical Example
    08:08
  • Higher Dimensional Distributions Overview
    01:22
  • ND Scatter Matrix - Practical Example
    09:50
  • ND Correlation - Practical Example
    05:20
  • 2D Histograms, Contours and KDE - Practical Example
    11:20
  • ND Scatter Probability - Practical Example
    12:13
  • Exploratory Data Analysis Summary
    02:57

  • Preview02:43
  • Mean Median Mode - Practical Example
    14:36
  • Widths - Practical Example
    05:09
  • Skewness and Kurtosis - Practical Example
    06:57
  • Percentiles - Practical Example
    07:52
  • Multivariate Distributions - Practical Example
    12:56
  • Summary
    03:45

  • Preview05:17
  • Introduction to Probability Distributions
    16:28
  • Probability Distributions - Practical Example
    10:14
  • Probability Functions and Empirical Distributions
    06:51
  • Empirical Distributions - Practical Example
    25:45
  • Introduction to Sampling and the Central Limit Theorem
    09:57
  • Sampling Distributions - Practical Example
    17:42
  • Extra Writeup: More resources on sampling distributions
    00:14
  • Central Limit Theorem - Practical Example
    10:39
  • Summary
    03:15

  • Introduction to Hypothesis Testing
    Preview02:17
  • Motivation Loaded Die - Practical Example
    08:24
  • Basic Tests
    13:11
  • Basic Tests Example - Asteroid Impacts
    20:17
  • Introduction to Proportion Testing
    02:44
  • Proportion Testing Example - Election Rigging
    12:01
  • Pearsons Chi2 Test - Practical Example
    09:08
  • Comparing Distributions - Kolmogorow-Smirnow and Anderson-Darling Tests
    12:41
  • Extra Writeup: All the ways to do A/B testing!
    00:11
  • Summary
    02:30

  • Conclusion
    09:56
  • Extra: Significance Hunting - What not to do!
    06:12
  • Extra: Introduction to Gaussian Proccesses
    25:41
  • Extra Prac - Cosmic Impact
    11:21
  • Extra Prac: Car Emission Standards
    08:39
  • Extra Prac: Diagnosing Diabetes
    16:20
  • Extra Prac: Numerical Uncertainty on Sales
    26:56

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.


  1. 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.


  2. 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.


  3. 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

Featured review

Tom Fischer
Tom Fischer
43 courses
6 reviews
Rating: 5.0 out of 5a year ago
More than just an awesome stats refresher. I really love the way they mix basics and more advanced topics based on Python. In each lesson, besides the topic I also learnt extra Python and stats things I did not expect - and I've been doing Python and Deep Learning for quite a while

Instructors

Samuel Hinton
Astrophysicist, Software Engineer and Presenter
Samuel Hinton
  • 4.6 Instructor Rating
  • 2,360 Reviews
  • 77,987 Students
  • 2 Courses

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!

Kirill Eremenko
Data Scientist
Kirill Eremenko
  • 4.5 Instructor Rating
  • 493,161 Reviews
  • 1,733,387 Students
  • 121 Courses

My name is Kirill Eremenko and I am super-psyched that you are reading this!

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

Hadelin de Ponteves
AI Entrepreneur
Hadelin de Ponteves
  • 4.5 Instructor Rating
  • 263,769 Reviews
  • 1,285,427 Students
  • 84 Courses

Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created 70+ top-rated educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached 1M+ students in 210 countries.

SuperDataScience Team
Helping Data Scientists Succeed
SuperDataScience Team
  • 4.5 Instructor Rating
  • 455,199 Reviews
  • 1,644,173 Students
  • 107 Courses

Hi there,

We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

The Real People at SuperDataScience

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