[UDEMY] Simulate, understand, & visualize data like a data scientist - [FTU]
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Files
| 1. Conclusions and how to learn more.mp4 | 4.9 MB | ||
| 1. Conclusions and how to learn more.vtt | 3.2 KB | ||
| 1. Course materials for this section (reader, MATLAB code, Python code).html | 102.4 B | ||
| 1. Overall goals of this course.mp4 | 7.8 MB | ||
| 1. Overall goals of this course.vtt | 4.6 KB | ||
| 1. Proactive vs. reactive data science.mp4 | 6.2 MB | ||
| 1. Proactive vs. reactive data science.vtt | 4.4 KB | ||
| 1. Thanks and coupon for other courses.html | 102.4 B | ||
| 1.1 THANKS.pdf.pdf | 100.6 KB | ||
| 1.1 prodata_TimeSeriesNoise.zip.zip | 474.1 KB | ||
| 1.1 prodata_TimeSeriesSignals.zip.zip | 653.1 KB | ||
| 1.1 prodata_dataClusters.zip.zip | 279.1 KB | ||
| 1.1 prodata_dataDistributions.zip.zip | 305.1 KB | ||
| 1.1 prodata_descriptiveVisualizations.zip.zip | 237.3 KB | ||
| 1.1 prodata_forwardModels.zip.zip | 4.2 MB | ||
| 1.1 prodata_imageNoise.zip.zip | 654.2 KB | ||
| 1.1 prodata_imageSignals.zip.zip | 263.6 KB | ||
| 2. Clusters in 2D.mp4 | 10.9 MB | ||
| 2. Clusters in 2D.vtt | 6.5 KB | ||
| 2. Forward model 2D sheet.mp4 | 31.4 MB | ||
| 2. Forward model 2D sheet.vtt | 9.3 KB | ||
| 2. Image white noise.mp4 | 5.1 MB | ||
| 2. Image white noise.vtt | 2.8 KB | ||
| 2. Lines and edges.mp4 | 6.5 MB | ||
| 2. Lines and edges.vtt | 3.7 KB | ||
| 2. Mean, median, standard deviation, variance.mp4 | 12.3 MB | ||
| 2. Mean, median, standard deviation, variance.vtt | 8.2 KB | ||
| 2. Normal and uniform distributions.mp4 | 14.6 MB | ||
| 2. Normal and uniform distributions.vtt | 8.5 KB | ||
| 2. Seeded reproducible normal and uniform noise.mp4 | 9.6 MB | ||
| 2. Seeded reproducible normal and uniform noise.vtt | 5.2 KB | ||
| 2. Sharp transients.mp4 | 8.9 MB | ||
| 2. Sharp transients.vtt | 5.2 KB | ||
| 2. Understand data origins and features.mp4 | 5.4 MB | ||
| 2. Understand data origins and features.vtt | 4.4 KB | ||
| 2. Why and how to simulate data.mp4 | 8.9 MB | ||
| 2. Why and how to simulate data.vtt | 6.3 KB | ||
| 3. Checkerboard patterns and noise.mp4 | 5.2 MB | ||
| 3. Checkerboard patterns and noise.vtt | 3.3 KB | ||
| 3. Clusters in N-D.mp4 | 8.9 MB | ||
| 3. Clusters in N-D.vtt | 2.1 KB | ||
| 3. Interquartile range.mp4 | 8.2 MB | ||
| 3. Interquartile range.vtt | 4.5 KB | ||
| 3. Mixed overlapping forward models.mp4 | 18.4 MB | ||
| 3. Mixed overlapping forward models.vtt | 5 KB | ||
| 3. Pink noise (aka 1f aka fractal).mp4 | 12.1 MB | ||
| 3. Pink noise (aka 1f aka fractal).vtt | 6.5 KB | ||
| 3. QQ plot.mp4 | 10.9 MB | ||
| 3. QQ plot.vtt | 7.1 KB | ||
| 3. Sine patches and Gabor patches.mp4 | 9.2 MB | ||
| 3. Sine patches and Gabor patches.vtt | 4.9 KB | ||
| 3. Smooth transients.mp4 | 19.9 MB | ||
| 3. Smooth transients.vtt | 11.7 KB | ||
| 3. What is signal and what is noise.mp4 | 8.4 MB | ||
| 3. What is signal and what is noise.vtt | 3.9 KB | ||
| 3. Write down or sketch the important results.mp4 | 8.6 MB | ||
| 3. Write down or sketch the important results.vtt | 5.2 KB | ||
| 4. Brownian noise (aka random walk).mp4 | 8 MB | ||
| 4. Brownian noise (aka random walk).vtt | 4.6 KB | ||
| 4. Don_t give up -- every mistake is a learning opportunity!.mp4 | 4.7 MB | ||
| 4. Don_t give up -- every mistake is a learning opportunity!.vtt | 2.7 KB | ||
| 4. Example Simulate human brain (EEG) data.mp4 | 33.7 MB | ||
| 4. Example Simulate human brain (EEG) data.vtt | 15.5 KB | ||
| 4. Geometric shapes.mp4 | 7.3 MB | ||
| 4. Geometric shapes.vtt | 3.4 KB | ||
| 4. Histogram.mp4 | 6.4 MB | ||
| 4. Histogram.vtt | 3.9 KB | ||
| 4. Perlin noise in 2D.mp4 | 9.9 MB | ||
| 4. Perlin noise in 2D.vtt | 4.6 KB | ||
| 4. Poisson distribution.mp4 | 12.7 MB | ||
| 4. Poisson distribution.vtt | 7 KB | ||
| 4. Repeating sine, square, and triangle waves.mp4 | 8.3 MB | ||
| 4. Repeating sine, square, and triangle waves.vtt | 3.9 KB | ||
| 4. The importance of visualization.mp4 | 11.4 MB | ||
| 4. The importance of visualization.vtt | 8 KB | ||
| 5. Filtered 2D-FFT noise.mp4 | 8.5 MB | ||
| 5. Filtered 2D-FFT noise.vtt | 4 KB | ||
| 5. Log-normal distribution.mp4 | 6.3 MB | ||
| 5. Log-normal distribution.vtt | 3.9 KB | ||
| 5. Multicomponent oscillators.mp4 | 6.2 MB | ||
| 5. Multicomponent oscillators.vtt | 3.6 KB | ||
| 5. Multivariable correlated noise.mp4 | 13.2 MB | ||
| 5. Multivariable correlated noise.vtt | 7.9 KB | ||
| 5. Rings.mp4 | 3.8 MB | ||
| 5. Rings.vtt | 2.9 KB | ||
| 5. Violin plot.mp4 | 8.7 MB | ||
| 5. Violin plot.vtt | 5.7 KB | ||
| 6. Dipolar and multipolar chirps.mp4 | 15.4 MB | ||
| 6. Dipolar and multipolar chirps.vtt | 8.6 KB | ||
| 6. Measures of distribution quality (SNR and Fano factor).mp4 | 6.7 MB | ||
| 6. Measures of distribution quality (SNR and Fano factor).vtt | 4.4 KB | ||
| 7. Cohen_s d for separating distributions.mp4 | 10.7 MB | ||
| 7. Cohen_s d for separating distributions.vtt | 6.6 KB | ||
| Discuss.FreeTutorials.Us.html | 165.7 KB | ||
| FreeCoursesOnline.Me.html | 108.3 KB | ||
| FreeTutorials.Eu.html | 102.2 KB | ||
| How you can help Team-FTU.txt | 307.2 B | ||
| Torrent Downloaded From GloDls.to.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.org.txt | 512 B | ||
| ▲ 106 total files | |||
Description
Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python
Created by : Mike X Cohen
Last updated : 11/2018
Language : English
Caption (CC) : Included
Torrent Contains : 106 Files, 12 Folders
Course Source : https://www.udemy.com/suv-data-mxc/
What you'll learn
• Understand different categories of data
• Generate various datasets and modify them with parameters
• Use a multitude of data visualization 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
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 :
• >4 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 >3000 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.
Why I am qualified to teach this course :
My research and teaching involve evaluating, validating, extending, and developing novel data analysis methods for large-scale, multivariate and multidimensional datasets in neuroscience (brain science). Data generation, parameterization, and visualization are the most important skills in neuroscience data analysis methods, and I have >15 years of experience working on this topic, teaching this topic, and writing technical books on this topic (look them up on amazon!).
So what are you waiting for??
Watch the course introductory video to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up. And check out my website for the lowest-possible-price coupons for this and other courses.
I hope to see you soon in the course!
Mike
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.
For More Udemy Free Courses >>> http://www.freetutorials.eu
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.freetutorials.eu/ 
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