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Janani Ravi
The growth in computing power means that problems that were hard to solve earlier can now be tackled using numerical techniques. These are algorithms that seek to find numerical approximations to mathematical problems rather than use symbolic manipulation...
Read more
The growth in computing power means that problems that were hard to solve earlier can now be tackled using numerical techniques. These are algorithms that seek to find numerical approximations to mathematical problems rather than use symbolic manipulation...
Read more
The growth in computing power means that problems that were hard to solve earlier can now be tackled using numerical techniques. These are algorithms that seek to find numerical approximations to mathematical problems rather than use symbolic manipulation i.e. fit a formula. Symbolic manipulation is often very hard and may not always be tractable. Numerical analysis, on the other hand, allows us to give approximate answers to hard problems such as weather prediction, computing the trajectory of a spacecraft, setting prices for goods in real-time and in many other use cases. In this course, Solving Problems with Numerical Methods we will explore a wide variety of numerical techniques for different kinds of problems and learn how we can apply these techniques using the R programming language. First, you will learn how numerical methods are different from analytical methods and why it is important to be able to solve problems using numerical procedures. You will understand and work with direct and iterative numerical techniques to solve a system of linear equations and perform interpolation and extrapolation using a variety of different methods. Next, you will discover how graphs can be represented and the applications of graph algorithms in the real world. You will then move on to local search techniques to solve the N-queens problem. You will study variants of classic local search such as stochastic local search algorithms, simulated annealing and threshold accepting algorithms. These techniques allow locally bad moves to avoid getting stuck in local optima. Finally, you will explore how to formulate a linear programming problem by setting up your objective, constraints and decision variables and them implement a solution using R utilities. You will round off this course by understanding and implementing differentiation and integration using R programming. When you’re finished with this course, you will have the skills and knowledge to apply a variety of numerical procedures to solve mathematical problems using the R programming language.
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Builds a strong foundation for beginners who have no experience with numerical methods
Introduces direct and iterative numerical techniques for solving a wide range of mathematical problems
Provides hands-on experience with R programming for implementing numerical methods
Covers essential concepts such as graph algorithms, local search techniques, and linear programming
Led by instructors with expertise and experience in the field of numerical analysis
This course explicitly requires students to have a basic understanding of R programming
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Learners who complete Solving Problems with Numerical Methods will develop knowledge and skills
that may be useful to these careers:
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use advanced analytical techniques to help organizations make better decisions. They may work on projects to improve efficiency, reduce costs, or increase productivity. This course can help prepare you for a career as an Operations Research Analyst by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They may work on projects to improve customer service, identify fraud, or develop new products. This course can help you prepare for a career as a Data Scientist by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They may work for investment banks, hedge funds, or other financial institutions. This course can help prepare you for a career as a Financial Analyst by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk. They may work for insurance companies, pension funds, or other financial institutions. This course can help prepare you for a career as an Actuary by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work for a variety of industries, including technology, finance, and healthcare. This course can help prepare you for a career as a Software Engineer by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to make investment decisions. They may work for investment banks, hedge funds, or other financial institutions. This course can help prepare you for a career as a Quantitative Analyst by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumer behavior. They may work for a variety of organizations, including marketing firms, product development companies, and government agencies. This course can help prepare you for a career as a Market Researcher by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They may work for a variety of organizations, including consulting firms, corporations, and government agencies. This course can help prepare you for a career as a Business Analyst by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They may work for a variety of organizations, including government agencies, research institutions, and businesses. This course can help prepare you for a career as a Statistician by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They may work for a variety of organizations, including government agencies, research institutions, and businesses. This course can help prepare you for a career as an Economist by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Mathematician
Mathematician
Mathematicians study the properties of numbers, shapes, and other mathematical objects. They may work for a variety of organizations, including universities, research institutions, and businesses. This course can help prepare you for a career as a Mathematician by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and analyze algorithms. They may work for a variety of organizations, including technology companies, financial institutions, and government agencies. This course can help prepare you for a career as a Computer Scientist by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Physicist
Physicist
Physicists study the laws of nature. They may work for a variety of organizations, including universities, research institutions, and businesses. This course can help prepare you for a career as a Physicist by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Engineer
Engineer
Engineers design, build, and maintain structures and systems. They may work for a variety of organizations, including construction companies, manufacturing companies, and government agencies. This course can help prepare you for a career as an Engineer by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience will be essential for success in this field.
Biologist
Biologist
Biologists study living organisms. They may work for a variety of organizations, including universities, research institutions, and businesses. This course may be useful for a career as a Biologist by providing you with a strong foundation in numerical methods. You will learn how to solve problems using both direct and iterative techniques, and you will gain experience with a variety of numerical algorithms. This knowledge and experience may be helpful for success in this field.
For more career information including salaries, visit:
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Reading list
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Covers numerical methods for solving evolutionary differential equations, a topic of great importance in scientific computing. It is written by leading experts in the field.
Focuses on finite difference methods for solving partial differential equations. It good choice for students and researchers interested in learning about this specific topic.
Focuses on numerical methods for bifurcation problems, a specific area of differential equations where solutions change qualitatively as a parameter is varied. It is written by an expert in the field, with a focus on practical applications.
Is designed for undergraduate students in engineering and science. It provides a clear and concise introduction to numerical methods.
Provides a comprehensive overview of numerical optimization methods. It good choice for students and researchers interested in learning about this specific topic.
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Provides a comprehensive overview of numerical analysis, covering a wide range of topics from basic concepts to advanced techniques. It is suitable for both undergraduate and graduate students in mathematics, engineering, and other disciplines.
Classic in the field of numerical methods and provides a comprehensive collection of algorithms for solving a wide range of mathematical problems. It valuable resource for researchers and practitioners in many fields.
Presents numerical methods for stochastic differential equations, which are essential for modeling random phenomena in various fields. It is written by leading experts in the field and includes both theoretical background and practical algorithms.
Focuses on numerical methods for solving linear algebra problems. It good choice for students and researchers interested in learning about this specific topic.
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Classic Russian textbook on numerical methods. It provides a comprehensive overview of the subject, from basic concepts to advanced techniques. It is suitable for both undergraduate and graduate students.
Is another classic Russian textbook on numerical methods. It focuses on the mathematical foundations of numerical methods and is suitable for advanced undergraduate and graduate students.
Focuses on numerical methods for linear algebra problems. It good choice for students and researchers interested in learning about this specific topic.
Provides a comprehensive overview of numerical methods for solving partial differential equations. It good choice for students and researchers interested in learning about this specific topic.
Teaches readers how to use R effectively for data analysis and visualization. It covers a wide range of topics, from data manipulation and cleaning to statistical modeling and graphics.
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Collection of recipes for solving common problems in R. It covers a wide range of topics, from data manipulation and cleaning to statistical modeling and graphics.
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Is an introduction to R for non-programmers. It covers the basics of R, such as data manipulation, cleaning, and visualization.
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Guide to creating and using R packages. It covers topics such as package design, testing, and distribution.
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Comprehensive guide to the advanced features of R. It covers topics such as object-oriented programming, functional programming, and high-performance computing.
Practical guide to using R for data science. It covers topics such as data wrangling, exploratory data analysis, and machine learning.
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Provides a comprehensive overview of the R programming language, covering its syntax, data structures, and functions. It is an excellent resource for beginners who want to learn the basics of R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/93ouad/solving
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Time
3 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Statistical and Mathematical Models with R. It's comprised of nine courses:
- Understanding Statistical Models and Mathematical Models
- Solving Problems with Numerical Methods (viewing)
- Applying the Mathematical MASS Model with R
- Applying Differential Equations and Inverse Models with R
- Performing Dimension Analysis with R
- Applying Linear Algebra with R
- Building Statistical Summaries with R
- Implementing Bootstrap Methods in R
- Implementing Monte Carlo Method in R
Good to know
Builds a strong foundation for beginners who have no experience with numerical methods
Introduces direct and iterative numerical techniques for solving a wide range of mathematical problems
Provides hands-on experience with R programming for implementing numerical methods
Covers essential concepts such as graph algorithms, local search techniques, and linear programming
Led by instructors with expertise and experience in the field of numerical analysis
This course explicitly requires students to have a basic understanding of R programming
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