Generate and visualize data in Python and MATLAB

Generate and visualize data in Python and MATLAB, available at $64.99, has an average rating of 4.65, with 46 lectures, based on 365 reviews, and has 20256 subscribers.

You will learn about 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 This course is ideal for individuals who are Data scientists who want to learn how to generate data or Statisticians who want to evaluate and validate methods or Someone who wants to improve their MATLAB skills or Someone who wants to improve their Python skills or Scientists who want a better understanding of data characteristics or Someone looking for tools to better understand data or Anyone who wants to learn how to visualize data It is particularly useful for Data scientists who want to learn how to generate data or Statisticians who want to evaluate and validate methods or Someone who wants to improve their MATLAB skills or Someone who wants to improve their Python skills or Scientists who want a better understanding of data characteristics or Someone looking for tools to better understand data or Anyone who wants to learn how to visualize data.

Enroll now: Generate and visualize data in Python and MATLAB

Summary

Title: Generate and visualize data in Python and MATLAB

Price: $64.99

Average Rating: 4.65

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 datafrom 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 datain 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!

Course Curriculum

Chapter 1: Introductions

Lecture 1: Following along in Python, MATLAB, or Octave

Lecture 2: Overall goals of this course

Lecture 3: Why and how to simulate data

Lecture 4: What is signal and what is noise?

Lecture 5: The importance of visualization

Chapter 2: Descriptive statistics and basic visualizations

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Mean, median, standard deviation, variance

Lecture 3: Histogram

Lecture 4: Interquartile range

Lecture 5: Violin plot

Chapter 3: Data distributions

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Normal and uniform distributions

Lecture 3: QQ plot

Lecture 4: Poisson distribution

Lecture 5: Log-normal distribution

Lecture 6: Measures of distribution quality (SNR and Fano factor)

Lecture 7: Cohens d for separating distributions

Chapter 4: Time series signals

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Sharp transients

Lecture 3: Smooth transients

Lecture 4: Repeating: sine, square, and triangle waves

Lecture 5: Multicomponent oscillators

Lecture 6: Dipolar and multipolar chirps

Chapter 5: Time series noise

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Seeded reproducible normal and uniform noise

Lecture 3: Pink noise (aka 1/f aka fractal)

Lecture 4: Brownian noise (aka random walk)

Lecture 5: Multivariable correlated noise

Chapter 6: Image signals

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Lines and edges

Lecture 3: Sine patches and Gabor patches

Lecture 4: Geometric shapes

Lecture 5: Rings

Chapter 7: Image noise

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Image white noise

Lecture 3: Checkerboard patterns and noise

Lecture 4: Perlin noise in 2D

Lecture 5: Filtered 2D-FFT noise

Chapter 8: Data clustering in space

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Clusters in 2D

Lecture 3: Clusters in N-D

Chapter 9: Spatiotemporal structure using forward models

Lecture 1: Course materials for this section (reader, MATLAB code, Python code)

Lecture 2: Forward model: 2D sheet

Lecture 3: Mixed overlapping forward models

Lecture 4: Example: Simulate human brain (EEG) data

Chapter 10: Bonus section

Lecture 1: Bonus lecture

Instructors

Mike X Cohen
Educator and writer

Rating Distribution

1 stars: 3 votes

2 stars: 6 votes

3 stars: 27 votes

4 stars: 95 votes

5 stars: 234 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!