Generate and visualize data in Python and MATLAB
What you'll learn
- Understand different categories of data
- Generate various datasets and modify them with parameters
- Visualize data using a multitude of techniques
- Generate data from distributions, trigonometric functions, and images
- Understand forward models and how to use them to generate data
- Improve MATLAB and Python programming skills
- Interest in data
- High-school math
- Basic programming familiarity (MATLAB or Python)
- Familiarity with power spectra from the Fourier transform
Data science is quickly becoming one of the most important skills in industry, academia, marketing, and science. Most data-science courses teach analysis methods, but there are many methods; which method do you use for which data? The answer to that question comes from understanding data. That is the focus of this course.
What you will learn in this course:
You will learn how to generate data from the most commonly used data categories for statistics, machine learning, classification, and clustering, using models, equations, and parameters. This includes distributions, time series, images, clusters, and more. You will also learn how to visualize data in 1D, 2D, and 3D.
All videos come with MATLAB and Python code for you to learn from and adapt!
This course is for you if you are an aspiring or established:
Computer scientist (MATLAB and/or Python)
Signal processor or image processor
Curious independent learner!
What you get in this course:
>6 hours of video lectures that include explanations, pictures, and diagrams
pdf readers with important notes and explanations
Exercises and their solutions
MATLAB code and Python code
With >4000 lines of MATLAB and Python code, this course is also a great way to improve your programming skills, particularly in the context of data analysis, statistics, and machine learning.
What do you need to know before taking this course?
You need some experience with either Python or MATLAB programming. You don't need to be an expert coder, but if you are comfortable working with variables, for-loops, and basic plotting, then you already know enough to take this course!
Who this course is for:
- Data scientists who want to learn how to generate data
- Statisticians who want to evaluate and validate methods
- Someone who wants to improve their MATLAB skills
- Someone who wants to improve their Python skills
- Scientists who want a better understanding of data characteristics
- Someone looking for tools to better understand data
- Anyone who wants to learn how to visualize data
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