Generate and visualize data in Python and MATLAB from Udemy

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?

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.

What's inside

Learning objectives

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

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

Syllabus

Introductions

Following along in Python, MATLAB, or Octave

Overall goals of this course

Why and how to simulate data

Learn about the two most important distributions used in data science. Then see it in action in Python and MATLAB!

QQ sounds funny, right? But it is a powerful data visualization and inspection method.

Many physical and biological data distributions are characterized by Poisson. Learn how to simulate them in Python and MATLAB.

Log-normal data distributions come from combining other distributions. Hint: They're never negative!

Data quality is super-important in data science. Here you will learn the math, Python, and MATLAB methods for measuring data distribution quality.

You're probably thinking that I'm promoting my own method. But it's a different Cohen's D. Still a good metric, though!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience with both MATLAB and Python, which are essential tools in data science, statistics, and machine learning, allowing learners to apply concepts directly

Covers a wide range of data categories, including distributions, time series, images, and clusters, which are fundamental for various applications in data science and related fields

Includes exercises and solutions, which allows learners to test their understanding and reinforce their knowledge of data generation and visualization techniques

Requires some experience with either Python or MATLAB programming, which may pose a barrier to entry for individuals with no prior coding background

Focuses on generating data from commonly used data categories, which is a crucial skill for understanding data and selecting appropriate analysis methods in various domains

Explores descriptive statistics and basic visualizations, which are fundamental concepts for understanding and summarizing data in various fields

Reviews summary

Practical data generation & visualization

According to learners, this course provides a strong foundationpositive in generating and visualizing data using both Python and MATLABneutral. Many highlight the clear explanationspositive and the value of having code examples readily availablepositive in both languages. Students appreciate the practical approach and find the exercises helpfulpositive for reinforcing concepts. While broadly praised for its introductory quality, some reviewers note it may be more suitable for beginnerswarning and lacks depth for advanced userswarning seeking complex topics or optimizations.

Exercises reinforce learning effectively.

"The exercises were helpful for practicing the concepts covered in the videos."

"I found the exercises to be a good way to solidify my understanding."

"Working through the exercises really helped the material stick."

"The provided exercises allowed me to immediately apply what I learned."

Excellent introduction for those new to data generation/viz.

"This course is a very good starting point for generating and visualizing data."

"As someone relatively new to this area, I found this course very accessible and informative."

"Provides a solid introduction to the subject matter."

"It's a great foundational course if you are just starting out."

Instructor explains concepts clearly and concisely.

"The instructor does a great job explaining complex topics in a simple way."

"Explanations were very clear, making it easy to follow along even with some new concepts."

"I understood the concepts presented thanks to the clear and concise lectures."

"The way the material is presented makes it very easy to digest."

Provides code examples in both Python and MATLAB.

"It was excellent to have the code in both Python and MATLAB as I use both and it's sometimes hard to 'translate'!"

"Providing all the code in both Python and MATLAB is fantastic and allows me to apply this to my workflow."

"I really appreciated the availability of code snippets in both languages. This makes it very versatile for different users."

"Having the code provided in both Python and MATLAB is a huge plus for this course."

Could be too basic for more experienced users.

"While good for beginners, intermediate or advanced users might find it a bit superficial."

"I was hoping for more in-depth coverage on certain topics, but it mostly covers the basics."

"If you already have a lot of experience with data generation, this might not offer much new."

"Could benefit from more complex examples or discussion of advanced techniques."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generate and visualize data in Python and MATLAB with these activities:

Review Descriptive Statistics

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Reinforce your understanding of descriptive statistics, which are fundamental for data analysis and visualization.

Browse courses on Descriptive Statistics

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Review definitions of mean, median, standard deviation, and variance.
Work through practice problems calculating these statistics on sample datasets.
Use Python or MATLAB to calculate descriptive statistics on datasets.

Brush Up on Basic Plotting in Python/MATLAB

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Practice basic plotting techniques to prepare for visualizing generated data in the course.

Browse courses on Plotting

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Review the syntax for creating line plots, scatter plots, and histograms.
Practice creating plots from sample data using both Python and MATLAB.
Experiment with different plot customizations (colors, labels, titles).

Read 'Data Science from Scratch'

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Gain a broader understanding of data science principles and Python programming for data analysis.

View Data Science from Scratch: First Principles... on Amazon

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Read the chapters on data visualization and statistics.
Work through the code examples in the book.
Apply the concepts learned to the course materials.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Generate and Plot Basic Functions

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Practice generating and plotting sine waves, square waves, and other basic functions in both Python and MATLAB.

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Write code to generate sine, square, and triangle waves with varying frequencies and amplitudes.
Plot the generated functions using matplotlib (Python) and MATLAB's plotting functions.
Experiment with different plotting styles and customizations.

Explore 'Python Data Science Handbook'

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Deepen your understanding of Python data science tools and techniques.

View Python Data Science Handbook: Essential Tools... on Amazon

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Review the chapters on NumPy, Pandas, and Matplotlib.
Experiment with the code examples in the book.
Apply the concepts learned to the course projects.

Create a Data Visualization Portfolio

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Showcase your data generation and visualization skills by creating a portfolio of visualizations from the course.

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Select several datasets generated during the course.
Create compelling visualizations of these datasets using Python or MATLAB.
Write a brief description of each visualization, explaining the data and the insights it reveals.
Compile the visualizations and descriptions into a portfolio.

Simulate and Visualize Sensor Data

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Apply your knowledge to simulate and visualize data from a hypothetical sensor system.

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Choose a type of sensor (e.g., temperature, pressure, accelerometer).
Research the characteristics of the sensor and the type of data it produces.
Develop a forward model to simulate the sensor data, including noise and other artifacts.
Visualize the simulated data using appropriate plotting techniques.

Career center

Learners who complete Generate and visualize data in Python and MATLAB will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses data analysis, statistics, and machine learning to derive insights from data and solve complex problems. This course helps you understand how to generate data from common data categories used in machine learning. Data scientists need to use effective visualization techniques, and this course allows you to visualize data in one, two, and three dimensions. The course provides significant practical experience with over 4000 lines of Python and MATLAB code, which will help aspiring data scientists improve their programming skills. The course may be particularly useful for understanding how to generate and manipulate datasets for machine learning applications.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, and interpret numerical data to identify trends and relationships. The core of a statistician's work involves understanding the nature and distribution of data, an area this course emphasizes. The course includes work with distributions, descriptive statistics, and basic visualizations. You'll also learn about QQ plots and data quality measurements. To prepare for a career as a statistician, you should take this course to learn how to generate data from various distributions. This course helps you understand data, which in turn helps you use the right analysis method for the right data.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine learning engineers design, develop, and deploy machine learning models. This course provides practical skills in generating and visualizing data using Python and MATLAB, which are essential tools for machine learning engineers. Understanding how to generate data from various distributions and categories, as taught in this course, enables you to create synthetic datasets for training and testing models. Working through the course's exercises and adapting the provided Python and MATLAB code will greatly assist machine learning engineers to manipulate, visualize, and understand their datasets. As a machine learning engineer, this course may be useful if you want to understand how to simulate data.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and insights. The focus of this course is on the understanding of data, which data analysts can benefit from. The course covers essential topics such as descriptive statistics, data distributions, and visualization techniques. The exercises and solutions included in the course help develop your proficiency in Python and MATLAB, which are commonly used tools in data analysis. This course is an appropriate introduction to data analysis, as it helps you generate and modify data with parameters.

See salaries and explore the career path for Data Analyst

Signal Processing Engineer

Signal processing engineers analyze and manipulate signals, such as audio, video, or sensor data, to extract useful information. To be successful as a signal processing engineer, you should know about time series signals, sharp transients, smooth transients, and various types of waves. This course covers these precise topics and more, including time series noise and multivariable correlated noise. Hands on experience with MATLAB and Python allows you to master signal processing techniques. This course helps you learn about data, so that you know which signal processing method to use for particular data types.

See salaries and explore the career path for Signal Processing Engineer

Image Processing Engineer

Image processing engineers develop algorithms and systems for processing and analyzing images. This course could be useful because it covers topics like image signals, lines and edges, geometric shapes, and image noise, which are all relevant to image processing. The material on Perlin noise and filtered 2D fast Fourier transform noise builds a basis for more advanced work in image manipulation. This course helps familiarize yourself with techniques for generating and modifying image data. If you are interested in becoming an image processing engineer, this course is for you.

See salaries and explore the career path for Image Processing Engineer

Quantitative Analyst

A quantitative analyst, often working in the finance industry, uses mathematical and statistical models to analyze financial data, assess risk, and develop trading strategies. This course might be applicable to those who want to learn how to generate and visualize data in Python and MATLAB, as these are common tools in quantitative analysis. The course covers data distributions and time series analysis, which are highly relevant. By learning how to simulate data, you can test and validate your models. A career as a quantitative analyst typically requires an advanced degree.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research scientists design and conduct experiments, analyze data, and publish their findings. This course helps build a foundation for research, as you learn necessary skills in data generation and visualization. Regardless of your scientific field, the ability to generate different types of data and visualize them effectively is crucial for designing experiments and interpreting results. This course may be useful for those who want to improve their data handling and programming skills in Python and MATLAB. Many research scientist positions require a doctoral degree.

See salaries and explore the career path for Research Scientist

Bioinformatician

A bioinformatician analyzes biological data using computational tools and techniques. The data analysis and visualization methods taught in this course may be useful for bioinformaticians. This course covers data distributions, statistics, and image analysis, all of which support a bioinformatician's daily tasks. Furthermore, the use of Python and MATLAB may be helpful. This course is particularly useful for learning to generate different types of data. Many bioinformatician positions require an advanced degree.

See salaries and explore the career path for Bioinformatician

Data Architect

Data architects design and build systems for storing and managing data. Though the course does not directly cover system design, this course may be useful since it provides a basis for understanding the various types of data that these systems will handle. Data architects need to understand data distributions and how data is structured to design efficient and scalable databases. This course helps familiarize yourself with different types of data. This course may also improve your Python and MATLAB skills.

See salaries and explore the career path for Data Architect

Business Intelligence Analyst

Business intelligence analysts analyze market trends, as well as sales and revenue data, to find areas of opportunity for a business. Although business intelligence analysts tend to use pre-existing software packages, they can still benefit from understanding the data they are working with. Understanding data distributions and visualizations, as covered in this course, helps ensure that the analyst draws appropriate insights and conclusions. This course may also be useful for improving Python skills. The course helps business intelligence analysts grow their careers.

See salaries and explore the career path for Business Intelligence Analyst

Software Developer

Software developers write code to create software applications. To be successful as a software developer, you should have experience with programming, including languages like Python and MATLAB. Though this course focuses on data generation and visualization, it also helps improve overall programming skills. This course might be useful if you intend to work on data-intensive applications. Software developers may benefit from the material on forward models and data clustering.

See salaries and explore the career path for Software Developer

Financial Analyst

Financial analysts evaluate financial data and provide investment recommendations. The content of this course may be useful in the work of financial analysts, as it covers data distributions, time series analysis, and visualization, which are all relevant to financial data. This course may be useful if you want a broader and deeper understanding of the data you use every day. By mastering generation and visualization techniques, you can gain new perspectives on data analysis. A career as a financial analyst typically requires a bachelor's degree.

See salaries and explore the career path for Financial Analyst

Technical Writer

Technical writers create documentation for technical products and services such as software and hardware. While the course does not focus on writing, this course may be helpful if you are writing about subjects like data science, statistics, or machine learning. By understanding how these technologies work, including the data generation and visualization aspects, you can communicate more effectively with your audience. This course provides some practical experience that helps you communicate complex technical concepts to others. This career may be useful in a specific niche.

See salaries and explore the career path for Technical Writer

Web Developer

Web developers design and build websites. While this course does not provide web development skills, this course may be useful if you need to work with data visualization on websites. The techniques for generating and visualizing data using Python and MATLAB can be applied to creating interactive data displays on the web. This course may also be useful if you want to deepen your knowledge of data. As a web developer, you can use your knowledge of this course to provide a richer experience for the user.

See salaries and explore the career path for Web Developer

Generate and visualize data in Python and MATLAB

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What's inside

Learning objectives

Syllabus

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Reviews summary

Practical data generation & visualization

Activities

Career center

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