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
What you'll learn
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.
Requirements
Basic knowledge of Python programming.
Understanding of fundamental mathematical concepts.
Description
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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