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Operations on Arrays with NumPy
Axel Sirota
Axel Sirota
This course will teach you how to leverage NumPy to up your game in scientific programming, and show you how to use
NumPy for numerical processing, including array indexing, math operations, and search.
Read more
This course will teach you how to leverage NumPy to up your game in scientific programming, and show you how to use NumPy for numerical processing, including array indexing, math operations, and search.
Read more
This course will teach you how to leverage NumPy to up your game in scientific programming, and show you how to use NumPy for numerical processing, including array indexing, math operations, and search.
Ideal for data scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and search. In this course, Operations on Arrays with NumPy, you’ll learn how to interact and manipulate NumPy Arrays at will. First, you’ll explore how to do indexing, slicing and mask with binary operators. Next, you’ll discover how to perform both linear algebra as well as statistical operations with NumPy arrays. Finally, you’ll learn how to sort and search in them any element. When you’re finished with this course, you’ll have the skills and knowledge of NumPy arrays needed to manipulate them in any application you may need.
Register for this course and see more details by visiting:
OpenCourser.com/course/rpgq45/operations
What's inside
Syllabus
Course Overview
Intoduction
Slicing and Indexing NumPy Arrays
Operating with NumPy Arrays
Read more
Syllabus
Course Overview
Intoduction
Slicing and Indexing NumPy Arrays
Operating with NumPy Arrays
Read more
Searching in NumPy Arrays
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data science skills, which are necessary for working with data and making informed decisions
Begins with fundamental data manipulation techniques, making it suitable for those new to the field
Examines statistical operations, which learners may use in professional roles
Facilitates array indexing and manipulation, crucial for working with arrays efficiently
Prepares learners for applying NumPy arrays in real-world scenarios, enhancing problem-solving abilities
Covers core principles of NumPy effectively, building a solid foundation for further learning
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Activities
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Operations on Arrays with NumPy.
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Career center
Learners who complete Operations on Arrays with NumPy will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Working with large sets of structured data is a key part of a Data Analyst's job. NumPy can help make the manipulation and analysis of such data much simpler and this course provides a good foundation in the use of NumPy. Once you enroll in this course, you'll discover how to index, slice, and operate on NumPy arrays. You'll also learn linear algebra, statistical operations, and how to sort and search in NumPy arrays.
Data Scientist
Data Scientist
NumPy is a foundational dependency among Data Scientists. This course can help you in the early stages of your Data Science career. By learning to use NumPy for numerical processing, including array indexing, math operations, and search, you'll lay a strong groundwork for future success.
Machine Learning Engineer
Machine Learning Engineer
NumPy is a core dependency in the field of Machine Learning. This course can help you build a foundation for your Machine Learning Engineering career by teaching you how to use NumPy for numerical processing, including array indexing, math operations, and search.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts often work with complex datasets that need to be carefully analyzed. NumPy is useful for working with such datasets, and this course can help provide a grounding in the use of NumPy.
Statistician
Statistician
Statisticians often use NumPy for data analysis. This course can provide a valuable grounding in the use of NumPy and help Statisticians perform their jobs more efficiently.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers must be able to manage large amounts of data and NumPy is a popular tool for doing just that. This course can help build the necessary skills for a career as a Quantitative Researcher.
Financial Analyst
Financial Analyst
Performing mathematical and statistical analyses of financial data is a fundamental task of a Financial Analyst. NumPy can help automate many of these operations, and this course can help develop the skills needed to do so.
Investment Analyst
Investment Analyst
Investment Analysts often use NumPy to perform data analysis and modeling. This course can help build the skills needed to work as an Investment Analyst and potentially accelerate career growth.
Actuary
Actuary
Actuaries use NumPy for financial computations and analysis. This course can provide a valuable grounding in the use of NumPy, which can help support Actuarial work.
Risk Analyst
Risk Analyst
Risk Analysts must be able to effectively work with data. NumPy can help Risk Analysts to perform their jobs more efficiently. This course can help build the skills for working with NumPy.
Data Engineer
Data Engineer
Working with big data often requires an understanding of NumPy. This course can provide the necessary grounding that may be required for success as a Data Engineer.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts often utilize NumPy for optimization and modeling. This course may be helpful for Operations Research Analysts who need to understand the fundamentals of working with NumPy.
Financial Risk Manager
Financial Risk Manager
Financial Risk Managers may find NumPy to be a useful tool for data analysis. This course can develop the skills required to use NumPy on the job.
Business Analyst
Business Analyst
Business Analysts use NumPy to process and analyze business data. This course can help in building skills that may be useful as a Business Analyst.
Software Engineer
Software Engineer
A Software Engineer must be proficient in many areas of programming. Many Software Engineers who work with data may find NumPy to be a useful library and this course may be helpful to them.
For more career information including salaries, visit:
OpenCourser.com/course/rpgq45/operations
Reading list
We've selected 14 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
Operations on Arrays with NumPy.
Comprehensive guide to NumPy. While this book does require some knowledge of Python programming to read and understand, it covers almost everything you need to know about NumPy. This book covers all the topics of this course as well as many more.
Hands-on guide to data science. It covers all the essential topics, including NumPy, pandas, matplotlib, and scikit-learn.
Comprehensive guide to data science with Python. It covers a wide range of topics, from the basics of Python to more advanced topics like machine learning and deep learning. While this book does cover NumPy, it is not as in-depth as some of the other books on this list.
Comprehensive guide to data analysis with Python and Pandas. It covers the basics of Python and Pandas, as well as more advanced topics like data wrangling, data visualization, and machine learning. While this book does cover NumPy, it is not as in-depth as some of the other books on this list.
Practical guide to data analysis with Pandas. It covers all the essential features of Pandas, such as data manipulation, cleaning, and visualization.
Is an introduction to scientific computing with Python. It covers the basics of Python, NumPy, and SciPy. This book is perfect for beginners who want to learn how to use Python for scientific computing.
Beginner's guide to NumPy. It covers the basics of NumPy, such as indexing, slicing, and mask with binary operators. This book is perfect for beginners who want to learn more about NumPy.
Save
Comprehensive guide to deep learning. It covers all the essential topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Comprehensive guide to statistical learning. It covers all the essential topics, including linear regression, logistic regression, and decision trees.
Gentle introduction to statistical learning. It covers all the essential topics, including linear regression, logistic regression, and decision trees.
Comprehensive guide to computer science. It covers all the essential topics, including computer architecture, operating systems, and programming languages.
Comprehensive guide to computer architecture. It covers all the essential topics, including computer organization, instruction set architecture, and memory systems.
Comprehensive guide to operating systems. It covers all the essential topics, including process management, memory management, and file systems.
Comprehensive guide to computer networking. It covers all the essential topics, including network architecture, protocols, and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rpgq45/operations
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Time
87 hours
Level
Intermediate
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Good to know
Develops data science skills, which are necessary for working with data and making informed decisions
Begins with fundamental data manipulation techniques, making it suitable for those new to the field
Examines statistical operations, which learners may use in professional roles
Facilitates array indexing and manipulation, crucial for working with arrays efficiently
Prepares learners for applying NumPy arrays in real-world scenarios, enhancing problem-solving abilities
Covers core principles of NumPy effectively, building a solid foundation for further learning
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