Python Numpy Programming With Coding Exercises

Python Numpy Programming With Coding Exercises - Printable Version

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Python Numpy Programming With Coding Exercises - AD-TEAM - 01-18-2025

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Python Numpy Programming With Coding Exercises
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 194.88 MB | Duration: 1h 32m

Master Numerical Computing and Data Analysis with NumPy Through Hands-On Coding

[b]What you'll learn[/b]

How to create and manipulate NumPy arrays for efficient numerical computing.

Techniques for performing mathematical operations and statistical analysis with NumPy.

Advanced array manipulations such as reshaping, indexing, and broadcasting.

Application of NumPy in solving linear algebra problems and integrating with other data analysis tools.

[b]Requirements[/b]

Basic knowledge of Python programming.

Understanding of fundamental mathematical concepts.

[b]Description[/b]

Welcome to Python NumPy Programming with Coding Exercises, a comprehensive course designed to teach you the essentials of numerical computing using the NumPy library. NumPy is a fundamental package for scientific computing in Python, providing support for arrays, matrices, and a wide range of mathematical functions. This course will guide you through the core functionalities of NumPy, enhancing your ability to perform efficient data manipulation and analysis.In today's data-driven world, proficiency in numerical computing is crucial for analyzing data, performing complex calculations, and building machine learning models. NumPy's powerful array operations and mathematical capabilities make it an indispensable tool for data scientists, analysts, and engineers. This course aims to equip you with practical skills and knowledge through hands-on coding exercises that reinforce learning and apply concepts to real-world problems.Throughout this course, you will cover:Introduction to NumPy and its array objects: Understand the basics of NumPy, including array creation, manipulation, and basic operations.Array operations and mathematical functions: Learn to perform arithmetic operations, statistical calculations, and algebraic manipulations with NumPy arrays.Advanced array manipulations: Explore topics such as indexing, slicing, reshaping, and broadcasting to handle complex data structures.Numerical methods and linear algebra: Apply NumPy for solving linear algebra problems, including matrix operations and decompositions.Data analysis and integration: Use NumPy for data cleaning, transformation, and integration with other libraries like pandas.Practical exercises: Apply your skills to solve real-world problems and work with datasets to reinforce learning and practice key concepts.By the end of this course, you will be proficient in using NumPy for numerical computing, enabling you to handle large datasets efficiently and perform advanced mathematical operations with ease.Instructor Introduction: Faisal Zamir is a seasoned Python developer and educator with over 7 years of experience in teaching and working with Python libraries. Faisal's expertise in numerical computing and his clear, practical teaching approach will guide you through the intricacies of NumPy, ensuring you gain valuable skills and insights.Certificate at the End of Course: Upon successful completion of the course, you will receive a certificate that validates your skills in Python NumPy programming, enhancing your professional profile.

Overview

Section 1: Introduction to NumPy

Lecture 1 Introduction to NumPy

Lecture 2 Lesson 01

Lecture 3 Coding Exercises

Section 2: Array Operations and Basic Mathematics

Lecture 4 Array Operations and Basic Mathematics

Lecture 5 Lesson 02

Lecture 6 Coding Exercises

Section 3: Working with Random Numbers

Lecture 7 Working with Random Numbers

Lecture 8 Lesson 03

Lecture 9 Coding Exercises

Section 4: Array Manipulation Techniques

Lecture 10 Array Manipulation Techniques

Lecture 11 Lesson 04

Lecture 12 Coding Exercises

Section 5: Understanding NumPy Data Types and Customization

Lecture 13 Understanding NumPy Data Types and Customization

Lecture 14 Lesson 05

Lecture 15 Coding Exercises

Section 6: Working with Statistical and Mathematical Functions

Lecture 16 Working with Statistical and Mathematical Functions

Lecture 17 Lesson 06

Lecture 18 Coding Exercises

Section 7: Working with Linear Algebra in NumPy

Lecture 19 Working with Linear Algebra in NumPy

Lecture 20 Lesson 07

Lecture 21 Coding Exercises

Section 8: Advanced Indexing and Slicing

Lecture 22 Advanced Indexing and Slicing

Lecture 23 Lesson 08

Lecture 24 Coding Exercises

Section 9: Performance Optimization and Best Practices

Lecture 25 Performance Optimization and Best Practices

Lecture 26 Lesson 09

Lecture 27 Coding Exercises

Section 10: Integration with Other Libraries and Real-World Applications

Lecture 28 Integration with Other Libraries and Real-World Applications

Lecture 29 Lesson 10

Lecture 30 Coding Exercises

Data scientists and analysts seeking to enhance their skills in numerical computing.,Python developers interested in mastering array operations and data manipulation.,Professionals and students aiming to apply mathematical and statistical techniques in their projects.

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