Python for Statistical Analysis
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
Featured review
Instructors
- 4.6 Instructor Rating
- 4,518 Reviews
- 90,678 Students
- 4 Courses
Hi, I'm Sam and I'm an astrophysicist, data scientist, robotics and software engineer, astronomer and public presenter.
My work right now is all about renewable energy. Battery assets, optimising their utilisation and trading energy in markets to cut out as many fossil fuel generators as humanly possible.
In academia, 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!
- 4.5 Instructor Rating
- 683,006 Reviews
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- 152 Courses
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
- 4.5 Instructor Rating
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- 111 Courses
Hi there,
We are the Ligency PR and Marketing team. You will be hearing from us when new 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 Ligency