Digital Signal Processing from Coursera

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

Good to know

Know what's good

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Explores foundational concepts in Digital Signal Processing, making it ideal for students with no prior knowledge

Taught by instructors Paolo Prandoni and Martin Vetterli, both recognized experts in the field

Provides hands-on examples and demonstrations to reinforce theoretical concepts

Students are expected to have proficiency in basic calculus and linear algebra

May require additional resources for learners without a strong foundation in these mathematical concepts

Focuses on building a solid foundation in DSP, which can serve as a stepping stone for advanced courses or practical applications

Reviews summary

Solid dsp foundations

This course gets into the core basics of digital signal processing and lays a solid foundation in the fundamentals. Students appreciate the clear slide presentations and support. The mathematical explanations do require some background experience so new students may want to look elsewhere to get started.

Perfect slides and support

"Perfect slides and support"

Good breakdown of basics

"course where you can surely learn a lot basic about signals and their processing"

Rather math-heavy for an intro

"A bit harder on the math side than other signal processing MOOCs that I have taken."

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 Digital Signal Processing with these activities:

Follow a tutorial on DSP basics

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Following a tutorial on DSP basics will help you get started with the fundamentals of the subject.

Browse courses on Digital Signal Processing

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Find a tutorial that covers the basics of DSP.
Follow the tutorial step-by-step and complete the exercises.

Review 'Digital Signal Processing Using MATLAB'

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This book provides a comprehensive overview of DSP concepts and techniques, and will help you build a strong foundation for the course.

View Digital Signal Processing Using Matlab : A... on Amazon

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Read the chapters that cover the topics you are studying in the course.
Work through the examples and exercises in the book.

Refresh Calculus skills

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Reviewing basic Calculus principles will help you more easily grasp the concepts of Digital Signal Processing.

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Review your notes from a previous Calculus course.
Complete practice problems on derivatives and integrals.
Watch online tutorials on Calculus concepts.

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Practice using Python for DSP

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Practicing coding in Python will help you apply the DSP concepts you learn in the course.

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Install Python and the necessary libraries.
Work through the Python notebooks provided in the course.

Solve DSP problems on LeetCode

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Solving DSP problems on LeetCode will test your understanding of the concepts and help you develop your problem-solving skills.

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Create a LeetCode account.
Search for DSP problems and start solving them.

Attend a DSP workshop

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Attending a DSP workshop will expose you to new ideas and techniques, and help you connect with other DSP professionals.

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Research DSP workshops in your area.
Register for a workshop that aligns with your interests.
Attend the workshop and actively participate in the activities.

Create a visual representation of a DSP algorithm

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Creating a visual representation of a DSP algorithm will help you understand how it works and how to implement it.

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Choose a DSP algorithm to visualize.
Determine the key steps and data flow of the algorithm.
Create a visual representation using a tool like Draw.io or Lucidchart.

Create a DSP project

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Creating a DSP project will allow you to apply the concepts you learn in the course to a real-world problem.

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Define the scope of your project.
Design and implement your DSP algorithm.
Test and evaluate your project.

Career center

Learners who complete Digital Signal Processing will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you would be responsible for collecting and analyzing large datasets to help businesses make smarter decisions. This course would help you build a strong foundation in the mathematical and computational techniques used in data science. You would learn how to use Python to clean and analyze data, build machine learning models, and communicate your findings.

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Machine Learning Engineer

As a Machine Learning Engineer, you would be responsible for developing and deploying machine learning models. This course would help you build a foundation in the theory and practice of machine learning. You would learn how to use Python to build and train machine learning models, and how to evaluate their performance.

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Data Analyst

As a Data Analyst, you would be responsible for collecting, cleaning, and analyzing data to help businesses understand their customers and make better decisions. This course would help you build a strong foundation in the statistical and computational techniques used in data analysis. You would learn how to use Python to clean and analyze data, and how to visualize your findings.

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Software Engineer

As a Software Engineer, you would be responsible for designing, developing, and maintaining software systems. This course would help you build a foundation in the theory and practice of software engineering. You would learn how to use Python to write clean and efficient code, and how to debug and test software systems.

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Quantitative Analyst

As a Quantitative Analyst, you would be responsible for using mathematical and statistical models to analyze financial data and make investment decisions. This course would help you build a strong foundation in the mathematical and computational techniques used in quantitative finance. You would learn how to use Python to analyze financial data and build financial models.

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Business Analyst

As a Business Analyst, you would be responsible for analyzing business processes and identifying ways to improve them. This course would help you build a foundation in the techniques used in business analysis. You would learn how to use Python to collect and analyze data, and how to communicate your findings.

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Operations Research Analyst

As an Operations Research Analyst, you would be responsible for using mathematical and statistical models to solve business problems. This course would help you build a foundation in the techniques used in operations research. You would learn how to use Python to build and solve mathematical models.

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Financial Analyst

As a Financial Analyst, you would be responsible for analyzing financial data and making investment recommendations. This course would help you build a foundation in the financial markets and the techniques used in financial analysis. You would learn how to use Python to analyze financial data and build financial models.

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Market Research Analyst

As a Market Research Analyst, you would be responsible for collecting and analyzing data about consumer behavior. This course would help you build a foundation in the techniques used in market research. You would learn how to use Python to collect and analyze data, and how to communicate your findings.

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Product Manager

As a Product Manager, you would be responsible for developing and managing products. This course would help you build a foundation in the techniques used in product management. You would learn how to use Python to collect and analyze data about user behavior, and how to use this data to improve your products.

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User Experience Designer

As a User Experience Designer, you would be responsible for designing and testing user interfaces. This course would help you build a foundation in the techniques used in user experience design. You would learn how to use Python to collect and analyze data about user behavior, and how to use this data to improve your designs.

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Web Developer

As a Web Developer, you would be responsible for designing and developing websites. This course would help you build a foundation in the techniques used in web development. You would learn how to use Python to build and maintain websites.

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Database Administrator

As a Database Administrator, you would be responsible for managing and maintaining databases. This course would help you build a foundation in the techniques used in database administration. You would learn how to use Python to create and manage databases.

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Systems Administrator

As a Systems Administrator, you would be responsible for managing and maintaining computer systems. This course would help you build a foundation in the techniques used in systems administration. You would learn how to use Python to manage and maintain computer systems.

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Network Administrator

As a Network Administrator, you would be responsible for managing and maintaining computer networks. This course would help you build a foundation in the techniques used in network administration. You would learn how to use Python to manage and maintain computer networks.

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