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所在平台: Udemy |
课程主页: https://www.udemy.com/course/100-exercises-python-programming-data-science-numpy/
课程评论：没有评论
课程名称：Python数据科学与NumPy：百题精炼 课程概述： 本课程是一门以实践为导向、侧重练习的实用性课程，旨在帮助学习者提升Python在数据科学领域的技能，特别是对强大的NumPy库的掌握。课程内容深入讲解NumPy库在高效处理数值数据方面的各项功能。 课程特色： * **海量练习：** 每个章节都包含精心设计的练习，帮助学习者巩固对各个概念的理解。 * **实战模拟：** 学习者将有机会解决模拟数据科学家日常工作中遇到的真实问题。 * **详尽解答：** 每道练习题都附有详细的解决方案，帮助理解“如何做”以及“为何这样做”。 目标受众： 本课程适合处于数据科学学习不同阶段的学习者，包括： * 刚刚入门的数据科学初学者。 * 希望巩固现有知识或增加NumPy实践经验的资深数据科学家。 先修要求： 具备Python编程基础知识。 NumPy介绍： NumPy（Numerical Python）是Python科学计算的基础库，提供了对数组、矩阵的支持，以及大量用于操作这些数据结构的数学函数。本课程将系统讲解NumPy的各项核心特性，包括数组的创建、索引、切片、操作，以及数学和统计函数等。
The course "Python Data Science with NumPy: Over 100 Exercises" is a practical, exercise-oriented program aimed at individuals who want to strengthen their Python data science skills, with a particular focus on the powerful NumPy library. It caters to learners eager to dive deep into the functionalities that NumPy offers for handling numerical data efficiently.Each section of the course contains a set of carefully curated exercises designed to consolidate the learners' understanding of each concept. Participants will get to tackle real-life problems that simulate challenges faced by data scientists in their everyday roles. Each exercise is followed by a detailed solution, helping students understand not just the 'how' but also the 'why' of each solution.The "Python Data Science with NumPy: Over 100 Exercises" course is suited for individuals at various stages of their data science journey - from beginners just starting out, to more experienced data scientists looking to refresh their knowledge or gain more practice working with NumPy. The primary prerequisite is a basic understanding of Python programming.NumPy - Unleash the Power of Numerical Python!NumPy, short for Numerical Python, is a fundamental library for scientific computing in Python. It provides support for arrays, matrices, and a host of mathematical functions to operate on these data structures. This course is structured into various sections, each targeting a specific feature of the NumPy library, including array creation, indexing, slicing, and manipulation, along with mathematical and statistical functions.
