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
- Python basics
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
Created by Samuel Hinton, Ligency Team
Last updated 3/2021
Size: 2.77 GB