Statistics literacy for non-statisticians
In this short course, you will learn the meaning of key terms in statistics, such as p-value, ANOVA, variance, etc.
By the end of this course, you will feel more comfortable talking about and reading about commonly used statistical analysis methods.
Note that this course does not cover the math of the analyses, nor software to perform statistical analyses.
Who this course is for:
- People unfamiliar with statistics but who want to learn the basics!
- 05:22Types of data: categorical, numeric, etc
- 05:27Populations, samples, case reports, and anecdotes
- 05:18Visualizing data
- 05:34Measures of central tendency (mean, median, mode)
- 03:12Measures of dispersion (variance, standard deviation)
- 04:04Data normalizations
I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.
But you're here because of my teaching, so let me tell you about that:
I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.
I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.
Over 120,000 students have watched over 7,500,000 minutes of my courses. Come find out why!
I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)
By popular request, here are suggested course progressions for various educational goals:
MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing
Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python
Applied linear algebra: Complete Linear Algebra; Dimension Reduction
Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing