# Generate and visualize data in Python and MATLAB

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 46 lectures (6h 24m) | 2.20 GB

Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python

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’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

Introductions
1 Following along in Python, MATLAB, or Octave
2 Overall goals of this course
3 Why and how to simulate data
4 What is signal and what is noise
5 The importance of visualization

Descriptive statistics and basic visualizations
6 Course materials for this section (reader, MATLAB code, Python code)
7 Mean, median, standard deviation, variance
8 Histogram
9 Interquartile range
10 Violin plot

Data distributions
11 Course materials for this section (reader, MATLAB code, Python code)
12 Normal and uniform distributions
13 QQ plot
14 Poisson distribution
15 Log-normal distribution
16 Measures of distribution quality (SNR and Fano factor)
17 Cohen’s d for separating distributions

Time series signals
18 Course materials for this section (reader, MATLAB code, Python code)
19 Sharp transients
20 Smooth transients
21 Repeating sine, square, and triangle waves
22 Multicomponent oscillators
23 Dipolar and multipolar chirps

Time series noise
24 Course materials for this section (reader, MATLAB code, Python code)
25 Seeded reproducible normal and uniform noise
26 Pink noise (aka 1f aka fractal)
27 Brownian noise (aka random walk)
28 Multivariable correlated noise

Image signals
29 Course materials for this section (reader, MATLAB code, Python code)
30 Lines and edges
31 Sine patches and Gabor patches
32 Geometric shapes
33 Rings

Image noise
34 Course materials for this section (reader, MATLAB code, Python code)
35 Image white noise
36 Checkerboard patterns and noise
37 Perlin noise in 2D
38 Filtered 2D-FFT noise

Data clustering in space
39 Course materials for this section (reader, MATLAB code, Python code)
40 Clusters in 2D
41 Clusters in N-D

Spatiotemporal structure using forward models
42 Course materials for this section (reader, MATLAB code, Python code)
43 Forward model 2D sheet
44 Mixed overlapping forward models
45 Example Simulate human brain (EEG) data

Bonus section
46 Bonus lecture

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