Generate and visualize data in Python and MATLAB
4.8 (263 ratings)
19,075 students enrolled

# Generate and visualize data in Python and MATLAB

Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python
4.8 (263 ratings)
19,075 students enrolled
Created by Mike X Cohen
Last updated 8/2020
English
English [Auto]
Current price: \$20.99 Original price: \$29.99 Discount: 30% off
5 hours left at this price!
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This course includes
• 6.5 hours on-demand video
• 9 articles
• 8 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion
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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
Course content
Expand all 46 lectures 06:24:40
+ Introductions
5 lectures 28:19
Why and how to simulate data
05:42
What is "signal" and what is "noise"?
03:45
The importance of visualization
07:13
+ Descriptive statistics and basic visualizations
5 lectures 40:44
Course materials for this section (reader, MATLAB code, Python code)
00:02
Mean, median, standard deviation, variance
16:31
Histogram
07:15
Interquartile range
08:27
Preview 08:29
+ Data distributions
7 lectures 01:03:49
Course materials for this section (reader, MATLAB code, Python code)
00:02

Learn about the two most important distributions used in data science. Then see it in action in Python and MATLAB!

Normal and uniform distributions
15:35

QQ sounds funny, right? But it is a powerful data visualization and inspection method.

QQ plot
09:22

Many physical and biological data distributions are characterized by Poisson. Learn how to simulate them in Python and MATLAB.

Preview 11:40

Log-normal data distributions come from combining other distributions. Hint: They're never negative!

Log-normal distribution
08:14

Data quality is super-important in data science. Here you will learn the math, Python, and MATLAB methods for measuring data distribution quality.

Measures of distribution quality (SNR and Fano factor)
08:55

You're probably thinking that I'm promoting my own method. But it's a different Cohen's D. Still a good metric, though!

Cohen's d for separating distributions
10:01
+ Time series signals
6 lectures 01:06:38
Course materials for this section (reader, MATLAB code, Python code)
00:02
Sharp transients
12:29
Smooth transients
19:55
Repeating: sine, square, and triangle waves
10:28
Multicomponent oscillators
06:03
Dipolar and multipolar chirps
17:41
+ Time series noise
5 lectures 46:45
Course materials for this section (reader, MATLAB code, Python code)
00:02
Seeded reproducible normal and uniform noise
09:20
Brownian noise (aka random walk)
10:27
Multivariable correlated noise
11:58
+ Image signals
5 lectures 44:39
Course materials for this section (reader, MATLAB code, Python code)
00:02
Lines and edges
10:27
Sine patches and Gabor patches
12:24
Geometric shapes
11:49
Rings
09:57
+ Image noise
5 lectures 34:40
Course materials for this section (reader, MATLAB code, Python code)
00:02
Image white noise
09:19
Checkerboard patterns and noise
09:18
Perlin noise in 2D
08:55
Filtered 2D-FFT noise
07:06
+ Data clustering in space
3 lectures 18:25
Course materials for this section (reader, MATLAB code, Python code)
00:02
Clusters in 2D
13:32
Preview 04:51
+ Spatiotemporal structure using forward models
4 lectures 39:51
Course materials for this section (reader, MATLAB code, Python code)
00:04
Forward model: 2D sheet
12:40
Mixed overlapping forward models
10:20
Example: Simulate human brain (EEG) data
16:47
Requirements
• Interest in data
• High-school math
• Basic programming familiarity (MATLAB or Python)
• Familiarity with power spectra from the Fourier transform
Description

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:

• Data scientist

• Statistician

• Computer scientist (MATLAB and/or Python)

• Signal processor or image processor

• Biologist

• Engineer

• Student

• 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