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[FreeTutorials.Eu] [UDEMY] Simulate, Understand, & Visualize Data Like A Data Scientist - [FTU]
TORRENT SUMMARY
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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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FILE LIST
Filename
Size
1. Introductions/1. Overall goals of this course.mp4
7.8 MB
1. Introductions/1. Overall goals of this course.vtt
4.6 KB
1. Introductions/2. Why and how to simulate data.mp4
8.9 MB
1. Introductions/2. Why and how to simulate data.vtt
6.3 KB
1. Introductions/3. What is signal and what is noise.mp4
8.4 MB
1. Introductions/3. What is signal and what is noise.vtt
3.9 KB
1. Introductions/4. The importance of visualization.mp4
11.4 MB
1. Introductions/4. The importance of visualization.vtt
8 KB
10. How to become a proactive data scientist/1. Proactive vs. reactive data science.mp4
6.2 MB
10. How to become a proactive data scientist/1. Proactive vs. reactive data science.vtt
4.4 KB
10. How to become a proactive data scientist/2. Understand data origins and features.mp4
5.4 MB
10. How to become a proactive data scientist/2. Understand data origins and features.vtt
4.4 KB
10. How to become a proactive data scientist/3. Write down or sketch the important results.mp4
8.6 MB
10. How to become a proactive data scientist/3. Write down or sketch the important results.vtt
5.2 KB
10. How to become a proactive data scientist/4. Don_t give up -- every mistake is a learning opportunity!.mp4
4.7 MB
10. How to become a proactive data scientist/4. Don_t give up -- every mistake is a learning opportunity!.vtt
2.7 KB
11. Conclusions and how to learn more/1. Conclusions and how to learn more.mp4
4.9 MB
11. Conclusions and how to learn more/1. Conclusions and how to learn more.vtt
3.2 KB
12. Thanks! and coupon for other courses/1. Thanks and coupon for other courses.html
58 B
12. Thanks! and coupon for other courses/1.1 THANKS.pdf.pdf
100.6 KB
2. Descriptive statistics and basic visualizations/1. Course materials for this section (reader, MATLAB code, Python code).html
68 B
2. Descriptive statistics and basic visualizations/1.1 prodata_descriptiveVisualizations.zip.zip
237.3 KB
2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.mp4
12.3 MB
2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.vtt
8.2 KB
2. Descriptive statistics and basic visualizations/3. Interquartile range.mp4
8.2 MB
2. Descriptive statistics and basic visualizations/3. Interquartile range.vtt
4.5 KB
2. Descriptive statistics and basic visualizations/4. Histogram.mp4
6.4 MB
2. Descriptive statistics and basic visualizations/4. Histogram.vtt
3.9 KB
2. Descriptive statistics and basic visualizations/5. Violin plot.mp4
8.7 MB
2. Descriptive statistics and basic visualizations/5. Violin plot.vtt
5.7 KB
3. Data distributions/1. Course materials for this section (reader, MATLAB code, Python code).html
76 B
3. Data distributions/1.1 prodata_dataDistributions.zip.zip
305.1 KB
3. Data distributions/2. Normal and uniform distributions.mp4
14.6 MB
3. Data distributions/2. Normal and uniform distributions.vtt
8.5 KB
3. Data distributions/3. QQ plot.mp4
10.9 MB
3. Data distributions/3. QQ plot.vtt
7.1 KB
3. Data distributions/4. Poisson distribution.mp4
12.7 MB
3. Data distributions/4. Poisson distribution.vtt
7 KB
3. Data distributions/5. Log-normal distribution.mp4
6.3 MB
3. Data distributions/5. Log-normal distribution.vtt
3.9 KB
3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).mp4
6.7 MB
3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).vtt
4.4 KB
3. Data distributions/7. Cohen_s d for separating distributions.mp4
10.7 MB
3. Data distributions/7. Cohen_s d for separating distributions.vtt
6.6 KB
4. Time series signals/1. Course materials for this section (reader, MATLAB code, Python code).html
66 B
4. Time series signals/1.1 prodata_TimeSeriesSignals.zip.zip
653.1 KB
4. Time series signals/2. Sharp transients.mp4
8.9 MB
4. Time series signals/2. Sharp transients.vtt
5.2 KB
4. Time series signals/3. Smooth transients.mp4
19.9 MB
4. Time series signals/3. Smooth transients.vtt
11.7 KB
4. Time series signals/4. Repeating sine, square, and triangle waves.mp4
8.3 MB
4. Time series signals/4. Repeating sine, square, and triangle waves.vtt
3.9 KB
4. Time series signals/5. Multicomponent oscillators.mp4
6.2 MB
4. Time series signals/5. Multicomponent oscillators.vtt
3.6 KB
4. Time series signals/6. Dipolar and multipolar chirps.mp4
15.4 MB
4. Time series signals/6. Dipolar and multipolar chirps.vtt
8.6 KB
5. Time series noise/1. Course materials for this section (reader, MATLAB code, Python code).html
69 B
5. Time series noise/1.1 prodata_TimeSeriesNoise.zip.zip
474.1 KB
5. Time series noise/2. Seeded reproducible normal and uniform noise.mp4
9.6 MB
5. Time series noise/2. Seeded reproducible normal and uniform noise.vtt
5.2 KB
5. Time series noise/3. Pink noise (aka 1f aka fractal).mp4
12.1 MB
5. Time series noise/3. Pink noise (aka 1f aka fractal).vtt
6.5 KB
5. Time series noise/4. Brownian noise (aka random walk).mp4
8 MB
5. Time series noise/4. Brownian noise (aka random walk).vtt
4.6 KB
5. Time series noise/5. Multivariable correlated noise.mp4
13.2 MB
5. Time series noise/5. Multivariable correlated noise.vtt
7.9 KB
6. Image signals/1. Course materials for this section (reader, MATLAB code, Python code).html
70 B
6. Image signals/1.1 prodata_imageSignals.zip.zip
263.6 KB
6. Image signals/2. Lines and edges.mp4
6.5 MB
6. Image signals/2. Lines and edges.vtt
3.7 KB
6. Image signals/3. Sine patches and Gabor patches.mp4
9.2 MB
6. Image signals/3. Sine patches and Gabor patches.vtt
4.9 KB
6. Image signals/4. Geometric shapes.mp4
7.3 MB
6. Image signals/4. Geometric shapes.vtt
3.4 KB
6. Image signals/5. Rings.mp4
3.8 MB
6. Image signals/5. Rings.vtt
2.9 KB
7. Image noise/1. Course materials for this section (reader, MATLAB code, Python code).html
72 B
7. Image noise/1.1 prodata_imageNoise.zip.zip
654.2 KB
7. Image noise/2. Image white noise.mp4
5.1 MB
7. Image noise/2. Image white noise.vtt
2.8 KB
7. Image noise/3. Checkerboard patterns and noise.mp4
5.2 MB
7. Image noise/3. Checkerboard patterns and noise.vtt
3.3 KB
7. Image noise/4. Perlin noise in 2D.mp4
9.9 MB
7. Image noise/4. Perlin noise in 2D.vtt
4.6 KB
7. Image noise/5. Filtered 2D-FFT noise.mp4
8.5 MB
7. Image noise/5. Filtered 2D-FFT noise.vtt
4 KB
8. Data clustering in space/1. Course materials for this section (reader, MATLAB code, Python code).html
73 B
8. Data clustering in space/1.1 prodata_dataClusters.zip.zip
279.1 KB
8. Data clustering in space/2. Clusters in 2D.mp4
10.9 MB
8. Data clustering in space/2. Clusters in 2D.vtt
6.5 KB
8. Data clustering in space/3. Clusters in N-D.mp4
8.9 MB
8. Data clustering in space/3. Clusters in N-D.vtt
2.1 KB
9. Spatiotemporal structure using forward models/1. Course materials for this section (reader, MATLAB code, Python code).html
116 B
9. Spatiotemporal structure using forward models/1.1 prodata_forwardModels.zip.zip
4.2 MB
9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.mp4
31.4 MB
9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.vtt
9.3 KB
9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.mp4
18.4 MB
9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.vtt
5 KB
9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.mp4
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