[GigaCourse.com] Udemy - Generate and visualize data in Python and MATLAB1. Introductions
1. Following along in Python, MATLAB, or Octave.mp4 (45.5 MB)
1. Following along in Python, MATLAB, or Octave.srt (9.1 KB)
2. Overall goals of this course.mp4 (29.8 MB)
2. Overall goals of this course.srt (6.2 KB)
3. Why and how to simulate data.mp4 (24.8 MB)
3. Why and how to simulate data.srt (7.2 KB)
4. What is signal and what is noise.mp4 (26.0 MB)
4. What is signal and what is noise.srt (4.3 KB)
5. The importance of visualization.mp4 (32.4 MB)
5. The importance of visualization.srt (9.5 KB)
10. Bonus section
1. Bonus lecture.html (2.7 KB)
2. Descriptive statistics and basic visualizations
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_descriptives.zip (230.1 KB)
2. Mean, median, standard deviation, variance.mp4 (72.9 MB)
2. Mean, median, standard deviation, variance.srt (21.3 KB)
3. Histogram.mp4 (34.3 MB)
3. Histogram.srt (9.0 KB)
4. Interquartile range.mp4 (50.9 MB)
4. Interquartile range.srt (10.4 KB)
5. Violin plot.mp4 (38.6 MB)
5. Violin plot.srt (11.1 KB)
3. Data distributions
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_distributions.zip (199.2 KB)
2. Normal and uniform distributions.mp4 (82.8 MB)
2. Normal and uniform distributions.srt (20.4 KB)
3. QQ plot.mp4 (36.7 MB)
3. QQ plot.srt (11.8 KB)
4. Poisson distribution.mp4 (71.7 MB)
4. Poisson distribution.srt (15.9 KB)
5. Log-normal distribution.mp4 (40.9 MB)
5. Log-normal distribution.srt (10.4 KB)
6. Measures of distribution quality (SNR and Fano factor).mp4 (38.9 MB)
6. Measures of distribution quality (SNR and Fano factor).srt (11.6 KB)
7. Cohen's d for separating distributions.mp4 (52.1 MB)
7. Cohen's d for separating distributions.srt (12.7 KB)
4. Time series signals
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_timeSeriesSignals.zip (152.5 KB)
2. Sharp transients.mp4 (65.9 MB)
2. Sharp transients.srt (15.3 KB)
3. Smooth transients.mp4 (108.3 MB)
3. Smooth transients.srt (25.2 KB)
4. Repeating sine, square, and triangle waves.mp4 (70.1 MB)
4. Repeating sine, square, and triangle waves.srt (13.6 KB)
5. Multicomponent oscillators.mp4 (33.8 MB)
5. Multicomponent oscillators.srt (7.6 KB)
6. Dipolar and multipolar chirps.mp4 (110.3 MB)
6. Dipolar and multipolar chirps.srt (22.3 KB)
5. Time series noise
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_timeSeriesNoise.zip (162.7 KB)
2. Seeded reproducible normal and uniform noise.mp4 (61.6 MB)
2. Seeded reproducible normal and uniform noise.srt (12.2 KB)
3. Pink noise (aka 1f aka fractal).mp4 (93.1 MB)
3. Pink noise (aka 1f aka fractal).srt (18.9 KB)
4. Brownian noise (aka random walk).mp4 (57.3 MB)
4. Brownian noise (aka random walk).srt (14.2 KB)
5. Multivariable correlated noise.mp4 (65.2 MB)
5. Multivariable correlated noise.srt (15.3 KB)
6. Image signals
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_imageSignals.zip (150.4 KB)
2. Lines and edges.mp4 (54.8 MB)
2. Lines and edges.srt (13.7 KB)
3. Sine patches and Gabor patches.mp4 (86.0 MB)
3. Sine patches and Gabor patches.srt (16.9 KB)
4. Geometric shapes.mp4 (67.0 MB)
4. Geometric shapes.srt (15.8 KB)
5. Rings.mp4 (55.0 MB)
5. Rings.srt (14.0 KB)
7. Image noise
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_imageNoise.zip (310.8 KB)
2. Image white noise.mp4 (67.9 MB)
2. Image white noise.srt (12.5 KB)
3. Checkerboard patterns and noise.mp4 (48.8 MB)
3. Checkerboard patterns and noise.srt (12.5 KB)
4. Perlin noise in 2D.mp4 (56.4 MB)
4. Perlin noise in 2D.srt (11.3 KB)
5. Filtered 2D-FFT noise.mp4 (51.1 MB)
5. Filtered 2D-FFT noise.srt (9.3 KB)
8. Data clustering in space
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_dataClusters.zip (131.3 KB)
2. Clusters in 2D.mp4 (82.7 MB)
2. Clusters in 2D.srt (18.5 KB)
3. Clusters in N-D.mp4 (35.1 MB)
3. Clusters in N-D.srt (6.1 KB)
9. Spatiotemporal structure using forward models
1. Course materials for this section (reader, MATLAB code, Python code).html (0.1 KB)
1.1 genvisdata_forwardModels.zip (4.0 MB)
2. Forward model 2D sheet.mp4 (87.6 MB)
2. Forward model 2D sheet.srt (16.4 KB)
3. Mixed overlapping forward models.mp4 (90.1 MB)
3. Mixed overlapping forward models.srt (13.4 KB)
4. Example Simulate human brain (EEG) data.mp4 (122.5 MB)
4. Example Simulate human brain (EEG) data.srt (21.8 KB)
Readme.txt (0.9 KB)
[GigaCourse.com].url (0.0 KB)
Description
Udemy - Generate and visualize data in Python and MATLAB
Description
Generate and visualize data in Python and MATLAB 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?
Generate and visualize data in Python and MATLAB 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!
Created by Mike X Cohen Last updated 4/2020 English English [Auto-generated]
