Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 39 Minute(s)
Language
English
Taught by
Samuel Hinton, SuperDataScience Team, Ligency
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries
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, the extra 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.
Course Content
- 7 section(s)
- 58 lecture(s)
- Section 1 Introduction
- Section 2 Exploring Data Analysis
- Section 3 Characterising
- Section 4 Probability
- Section 5 Hypothesis Testing
- Section 6 Conclusion
- Section 7 Congratulations!! Don't forget your Prize :)
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
Skills covered in this course
Reviews
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JJohn Welchance
This course is the perfect example of "the instructor clearly knows what they're doing, they just don't know how to teach it." There are tons of copy/pasting code with barely any details on what's happening. There are also barely any real details on the distributions themselves outside of "let's do this" examples. Starting with plotting of data, without discussing any of the underlying reasons for the plotting, was also a weird choice. All-in-all, I would not consider this a "beginner" course, as there isn't enough explanation on the code or even the statistical methods to truly wrap your head around the concepts, if you already haven't done some of this yourself in some fashion. He also doesn't really correct himself on things other than silently placing something on the screen. I am very appreciative of the jupyter notebooks code, but there's also barely any comments in it to really understand the line-by-line nature. As well, much of the code is at least 5 years out of date, even for his own package he created, which makes some of the code impossible to replicate without knowing how to do it yourself beforehand.
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VVaishnavi Aggarwal
it was good and insightful
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JJanhavi Chhabra
great teaching methods
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DDorothy Tomlinson
Excellent explanation of statistics from a computational perspective. The case studies were very satisfying, too. Probably best suited for those that have previously learnt the basics in Python.
