- 8.5 hours on-demand video
- 1 article
- 36 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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
A general course overview - what we'll cover, how we'll cover it, and where you can get help if things go wrong!
To join the Facebook ground, check this link out: https://www.facebook.com/groups/superdatascience/
For the Python 2v3 links, see:
Let's talk about setting everything up. What python version we'll use and the different ways you can get it.
If you've downloaded anaconda, you should have everything you need to get started available right away, and if not, here is the updated link to the Anaconda tutorial I've hosted online (apologies, the link has changed from the one in the presentation):
If you've picked miniconda, you'll need to use conda to install dependencies. To do that in your base environment, execute
conda install numpy scipy matplotlib pandas jupyter scikit-learn
If you want a new environment for this course (called 'stats'), try this out
conda create -n stats python=3.7 numpy scipy matplotlib pandas jupyter scikit-learn
conda activate stats
We'll be working with a lot of datasets in the coming lectures. So before we jump into that, let's discuss the different ways we can load data into our code. No coding in this one, let's focus on the higher level for just a moment!
Empirical CDFs aren't the most useful visualisation tool, but boy will they come in handy later when we apply statistical tests, so let's cover them here. On top of that, let's also quickly look at panda's describe function, which will quickly become a staple of your workflow.
Let's refresh some basic probability theory, probabilistic identities and the difference between a probability density function and a probability mass function.
An introduction to hypothesis testing. After all, what does the phrase even mean?
- 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.
- 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