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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-data-science-numpy-pandas-exercises/
课程评论：没有评论
Coursera 课程《230+ Exercises - Python for Data Science - NumPy + Pandas》是一门为期实战的互动课程，旨在帮助学习者掌握 Python 数据科学的核心库 NumPy 和 Pandas。 课程内容涵盖： * **NumPy：** 作为 Python 科學計算的基礎包，课程将深入讲解 NumPy 的数组、矩阵运算、统计运算以及随机数生成等功能。通过大量的练习，学习者将熟练运用 NumPy 进行高效的数值计算。 * **Pandas：** 课程随后转向 Pandas，该库提供了高性能、易于使用的数据结构和数据分析工具。学习者将通过实践练习，掌握 Pandas 的数据操作、数据清洗和数据可视化技巧，为实际数据科学项目做好准备。 这门课程的每个练习都经过精心设计，旨在巩固关键概念和技能，为学习者打下坚实的数值数据处理和高级数据分析基础。完成课程后，学习者将对 NumPy 和 Pandas 有深刻的理解，并能自信地将它们应用于数据科学领域，为进一步学习或职业发展打下坚实基础。 该课程适合具备基础编程概念的 Python 初学者，同时对于希望更新技能或转向数据相关职位的专业人士也极具价值。 **NumPy - 释放数值 Python 的力量！** NumPy (Numerical Python) 是 Python 科學計算的核心庫，提供对数组、矩阵以及相关数学函数的高效支持。课程将分章节详细讲解 NumPy 的数组创建、索引、切片、操作以及各种数学和统计函数。 **Pandas - 数据赋能，洞察无限！** Pandas 是一个强大的开源 Python 库，提供易用的数据结构和数据分析工具。它被广泛应用于数据科学、分析和研究领域，用于数据处理、清洗、探索和分析。Pandas 引入了 Series（一维标记数组）和 DataFrame（二维标记数据表）两种主要数据结构，能够高效处理结构化数据。通过 Pandas، 学习者可以进行过滤、分组、排序、合并和统计计算等多种数据操作，并能与 Python 数据生态系统中的其他库无缝集成，使其成为数据整理和分析的通用工具。
This course is an interactive, hands-on course designed for those who are seeking to gain practical experience in data science tools in Python, specifically the NumPy and Pandas libraries. The course contains over 230 exercises that provide learners with a platform to practice and consolidate their knowledge.The course begins with NumPy, the fundamental package for scientific computing in Python, covering topics like arrays, matrix operations, statistical operations, and random number generation. Learners will practice the use of NumPy functionality through numerous exercises, gaining the proficiency needed for more complex data science tasks.The course then transitions to Pandas, a library providing high-performance, easy-to-use data structures, and data analysis tools for Python. Here, learners will practice manipulating, cleaning, and visualizing data with Pandas, reinforcing skills necessary for real-world data science projects.Each exercise is designed to reinforce key concepts and skills, building a strong foundation in handling numerical data and performing advanced data analysis tasks. At the end of the course, learners will have a deep understanding of these libraries and their applications to data science, enhancing their proficiency and readiness for further study or work in this exciting field.This course is suitable for beginners in Python who have a basic understanding of programming concepts. However, professionals looking to refresh their skills or transition into a data-oriented role may also find it beneficial.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.Pandas - Data Empowered, Insights Unleashed!Pandas is a powerful open-source library in Python that provides easy-to-use data structures and data analysis tools. It is widely used by data scientists, analysts, and researchers for data manipulation, cleaning, exploration, and analysis tasks. Pandas introduces two primary data structures, namely Series (one-dimensional labeled array) and DataFrame (two-dimensional labeled data table), which allow efficient handling of structured data. With Pandas, you can perform various data operations such as filtering, grouping, sorting, merging, and statistical computations. It also offers seamless integration with other libraries in the Python data ecosystem, making it a versatile tool for data wrangling and analysis.
