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Saravanan Dhandapani
Companies have been aggressively working to harvest their large quantities of data and derive meaningful results from them. The first and most important step is to understand how to represent that data and perform mathematical computations.
Read more
Companies have been aggressively working to harvest their large quantities of data and derive meaningful results from them. The first and most important step is to understand how to represent that data and perform mathematical computations.
Read more
Companies have been aggressively working to harvest their large quantities of data and derive meaningful results from them. The first and most important step is to understand how to represent that data and perform mathematical computations.
In today’s fast paced world, companies are relying heavily on the data that they have been collecting from their client base. This is of course a first step. They need to know how to transform this data and derive meaningful insights for future growth.
In this course, Advanced Operations on Arrays with NumPy, you’ll gain the ability to apply complex mathematical operations on the data.
First, you’ll explore the differences between Array copy and view, understand the differences between them, and learn when to use them.
Next, you’ll discover NumPy’s matrix library and learn how to construct matrices, manipulate matrices, and perform basic arithmetic operations.
Finally, you’ll learn how to represent systems of linear equations in the form of NumPy matrices, compute eigenvalues, and solve linear equations.
When you’re finished with this course, you’ll have the skills and knowledge of performing complex mathematical operations on your data needed to build data models and derive meaningful results.
Register for this course and see more details by visiting:
OpenCourser.com/course/2ocfcw/advanced
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What's inside
Syllabus
Course Overview
Exploring Array Copy and View in NumPy
Working with Matrices Using NumPy Matrix Library
Performing Complex Calculations Using NumPy Linear Algebra
Read more
Syllabus
Course Overview
Exploring Array Copy and View in NumPy
Working with Matrices Using NumPy Matrix Library
Performing Complex Calculations Using NumPy Linear Algebra
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a deeper dive into linear algebra operations
Builds upon foundational knowledge of arrays and matrices in NumPy
Suitable for individuals with prior experience in Python and NumPy
Exploration of advanced operations is relevant to data science and machine learning
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Save Advanced Operations on Arrays with NumPy to your list so you can find it easily later:
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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 Advanced Operations on Arrays with NumPy with these
activities:
Review Matrix Operations Concepts
Show steps
Strengthen your foundation in matrix operations before delving into NumPy.
Browse courses on
Matrix Operations
Show steps
-
Review notes or textbooks on matrix operations
-
Practice solving matrix problems
Review Linear Algebra Books
Show steps
Refresh your knowledge of linear algebra concepts covered in the course.
View
Linear Algebra for Everyone
on Amazon
Show steps
-
Read the chapters relevant to the course
-
Solve practice problems at the end of each chapter
-
Take notes and highlight important concepts
Solve NumPy Practice Problems
Show steps
Reinforce your understanding of NumPy array operations.
Browse courses on
NumPy
Show steps
-
Find practice problems online or in textbooks
-
Practice solving the problems
-
Check your solutions and identify areas for improvement
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a NumPy Tutorial
Show steps
Deepen your understanding of NumPy by explaining it to others.
Browse courses on
NumPy
Show steps
-
Choose a specific topic related to NumPy
-
Research and gather information about the topic
-
Create a tutorial in a chosen format (e.g., video, blog post)
Build a Data Analysis Project Using NumPy
Show steps
Apply your NumPy skills to a real-world data analysis project.
Show steps
-
Define the project goals and scope
-
Gather and prepare the data
-
Use NumPy to perform data analysis and visualization
-
Interpret the results and draw conclusions
Review Matrix Operations Concepts
Show steps
Strengthen your foundation in matrix operations before delving into NumPy.
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Review notes or textbooks on matrix operations
- Practice solving matrix problems
Review Linear Algebra Books
Show steps
Refresh your knowledge of linear algebra concepts covered in the course.
View
Linear Algebra for Everyone
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to the course
- Solve practice problems at the end of each chapter
- Take notes and highlight important concepts
Solve NumPy Practice Problems
Show steps
Reinforce your understanding of NumPy array operations.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Practice solving the problems
- Check your solutions and identify areas for improvement
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a NumPy Tutorial
Show steps
Deepen your understanding of NumPy by explaining it to others.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Choose a specific topic related to NumPy
- Research and gather information about the topic
- Create a tutorial in a chosen format (e.g., video, blog post)
Build a Data Analysis Project Using NumPy
Show steps
Apply your NumPy skills to a real-world data analysis project.
Show steps
Show steps
Show steps
- Define the project goals and scope
- Gather and prepare the data
- Use NumPy to perform data analysis and visualization
- Interpret the results and draw conclusions
Career center
Learners who complete Advanced Operations on Arrays with NumPy will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help organizations make informed decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for data analysts to master.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for machine learning engineers to master.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for data scientists to master.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for statisticians to master.
Actuary
Actuary
Actuaries are responsible for using mathematical and statistical models to assess risk and make financial decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for actuaries to master.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for financial analysts to master.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data and make investment decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for quantitative analysts to master.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for market researchers to master.
Biostatistician
Biostatistician
Biostatisticians are responsible for using statistical methods to analyze biological data. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for biostatisticians to master.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and statistical models to solve business problems. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for operations research analysts to master.
Computational Scientist
Computational Scientist
Computational Scientists are responsible for using computer models to simulate and analyze complex systems. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for computational scientists to master.
Geophysicist
Geophysicist
Geophysicists are responsible for studying the Earth's physical properties. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for geophysicists to master.
Physicist
Physicist
Physicists are responsible for studying the fundamental laws of nature. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for physicists to master.
Meteorologist
Meteorologist
Meteorologists are responsible for studying the weather and climate. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for meteorologists to master.
Astronomer
Astronomer
Astronomers are responsible for studying the stars, planets, and other celestial objects. This course will help you build a strong foundation in NumPy, a powerful Python library for numerical operations, which is essential for astronomers to master.
For more career information including salaries, visit:
OpenCourser.com/course/2ocfcw/advanced
Reading list
We've selected 20 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced Operations on Arrays with NumPy.
Provides a practical introduction to linear algebra with a focus on applications in data science, machine learning, and other fields. While it may not cover all the specific topics covered in this course, it would be a useful resource for understanding the practical applications of linear algebra.
Provides a comprehensive overview of data science concepts and techniques using Python. It covers topics such as data manipulation, visualization, machine learning, and deep learning. While it may be useful for some concepts covered in this course, it may be more valuable as a general reference for data science.
Provides a clear and concise introduction to linear algebra. While it may not cover all the specific topics covered in this course, it would be a useful resource for gaining a deeper understanding of the mathematical foundations.
Provides a comprehensive and accessible introduction to linear algebra. While it may cover more topics than what is covered in this course, it would be a useful resource for anyone who wants to gain a deeper understanding of the subject.
Save
Provides a clear and concise introduction to linear algebra. While it may not cover all the specific topics covered in this course, it would be a useful resource for gaining a deeper understanding of the mathematical foundations.
Provides a clear and concise introduction to linear algebra. While it may not cover all the specific topics covered in this course, it would be a useful resource for gaining a deeper understanding of the mathematical foundations.
Provides a clear and concise introduction to linear algebra. While it may not cover all the specific topics covered in this course, it would be a useful resource for gaining a deeper understanding of the mathematical foundations.
Provides a clear and concise introduction to linear algebra. While it may not cover all the specific topics covered in this course, it would be a useful resource for gaining a deeper understanding of the mathematical foundations.
Provides a comprehensive and advanced treatment of numerical linear algebra. While it may be more advanced than what is covered in this course, it would be a valuable resource for anyone who wants to gain a deeper understanding of the subject.
Provides a rigorous and comprehensive treatment of matrix analysis and applied linear algebra. While it may be more advanced than what is covered in this course, it would be a valuable resource for anyone who wants to gain a deeper understanding of the subject.
Provides a rigorous and comprehensive introduction to linear algebra. While it may be more advanced than what is covered in this course, it would be a valuable resource for anyone who wants to gain a deeper understanding of the subject.
Save
Provides a rigorous and comprehensive treatment of linear algebra. While it may be more advanced than what is covered in this course, it would be a valuable resource for anyone who wants to gain a deeper understanding of the subject.
Save
Provides a comprehensive treatment of the numerical methods used to solve linear systems, least squares problems, and eigenvalue problems. The book also provides detailed coverage of NumPy and its applications in matrix computations.
Provides a comprehensive treatment of the mathematical methods used in physics. The book covers a wide range of topics, including calculus, vector analysis, and differential equations. The book also provides detailed coverage of NumPy and its applications in physics.
Provides a comprehensive treatment of the mathematical methods used in machine learning. The book covers a wide range of topics, including linear algebra, probability, and optimization. The book also provides detailed coverage of NumPy and its applications in machine learning.
Save
Provides a comprehensive treatment of the deep learning methods used in industry. The book covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. The book also provides detailed coverage of NumPy and its applications in deep learning.
Provides a comprehensive treatment of the convex optimization methods used in industry. The book covers a wide range of topics, including linear programming, quadratic programming, and semidefinite programming. The book also provides detailed coverage of NumPy and its applications in convex optimization.
Provides a comprehensive treatment of the numerical optimization methods used in industry. The book covers a wide range of topics, including unconstrained optimization, constrained optimization, and global optimization. The book also provides detailed coverage of NumPy and its applications in numerical optimization.
Save
Provides a comprehensive treatment of the machine learning methods used in data mining. The book covers a wide range of topics, including data preprocessing, feature selection, and model evaluation. The book also provides detailed coverage of NumPy and its applications in data mining.
Provides a comprehensive treatment of the reinforcement learning methods used in industry. The book covers a wide range of topics, including Markov decision processes, dynamic programming, and Monte Carlo methods. The book also provides detailed coverage of NumPy and its applications in reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2ocfcw/advanced
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Time
48 hours
Level
Introductory
Via
Pluralsight
Instructor
Saravanan Dhandapani
Language
English
Good to know
Provides a deeper dive into linear algebra operations
Builds upon foundational knowledge of arrays and matrices in NumPy
Suitable for individuals with prior experience in Python and NumPy
Exploration of advanced operations is relevant to data science and machine learning
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