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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-python-course-all-level-mega-pack/
课程评论：没有评论
课程名称：掌握Python与数据科学：从零到高级技能 课程概述：这是一门全面的Python课程，旨在将您从Python编程基础带入高级数据科学技术，包括数据处理、可视化、统计以及机器学习。无论您是完全的初学者，还是希望提升Python和数据科学技能的人，这门课程都将通过实践练习和真实世界案例为您提供实用的知识。课程结束时，您将能够自信地编写Python程序，使用Pandas操作数据，通过Matplotlib创建有价值的可视化，甚至构建简单的机器学习模型。该课程非常适合有抱负的开发者、数据分析师或希望深入了解Python用于数据驱动任务的任何人。 课程内容： 第一部分：Python入门 - 讲座1：Python中的数据类型 了解Python的数据类型，包括整数、浮点数、字符串、列表、元组、集合和字典。通过实践例子和练习学习每种数据类型的常见操作。 第二部分：Python基本构造 - 讲座2：函数 学习如何在Python中定义函数、传递参数、返回值，以及理解变量作用域。通过实际练习帮助您构建和使用函数。 第三部分：NumPy入门 - 讲座3：使用NumPy进行数学运算 了解NumPy数组及其在科学计算中的重要性，通过实际练习探索基础操作和数学函数。 第四部分：NumPy高级 - 讲座4：NumPy与列表的比较 通过性能比较和实际例子了解NumPy数组和Python列表的关键区别。 - 讲座5：SciPy简介 探索SciPy及其生态系统，重点关注其在科学计算中的应用。 - 讲座6：子包聚类 深入SciPy的聚类子包，并在真实数据集上应用聚类技术。 第五部分：使用Pandas进行数据处理 - 讲座7：Pandas简介 了解Pandas及其强大的数据结构：Series和DataFrame。学习数据处理的重要性。 - 讲座8：Pandas中的DataFrame 学习如何创建、操作和筛选Pandas中的数据。 - 讲座9：合并、连接和串联 掌握使用合并、连接和串联函数进行数据组合的技巧。 - 讲座10：导入和分析数据集 发现从各种来源导入和探索数据的方法。 - 讲座11：清洗数据集 学习处理缺失数据、重复数据和异常值的技巧。 - 讲座12：操纵数据集 探索使用apply、map和groupby的高级操作技术。 - 讲座13：可视化数据集 使用Pandas的内置函数创建有洞察力的数据可视化。 第六部分：使用Matplotlib进行数据可视化 - 讲座14：什么是数据可视化？ 了解数据可视化的重要性，并探索不同类型的可视化及其使用场景。 - 讲座15：Matplotlib简介 动手学习Matplotlib，掌握基本绘图技巧。 - 讲座16-22：创建不同类型的图表 通过逐步指导和实践练习掌握创建线图、柱状图、散点图、直方图、箱形图、小提琴图、饼图、甜甜圈图和面积图。 第七部分：统计 - 讲座23：什么是数据？ 学习数据的基本概念、类型和数据收集方法。 - 讲座24：统计学简介 了解核心统计概念，包括描述性统计与推断统计的区别。 - 讲座25：抽样 深入抽样方法及其在统计学中的重要性。 - 讲座26：概率 学习基本概率概念和规则。 - 讲座27：概率分布 探索各种概率分布及其应用。 - 讲座28：推断统计 掌握假设检验和信心区间的数据推断方法。 第八部分：使用Python进行机器学习 - 讲座29：机器学习类型 介绍有监督学习、无监督学习和强化学习。 - 讲座30：机器学习的应用 探索机器学习在各行业中的实际应用。 - 讲座31：机器学习演示 遵循逐步指导，构建和评估简单的机器学习模型。 为什么选择这个课程？ 通过注册这门课程，您将建立扎实的Python编程和数据科学基础。获得使用行业标准库（如NumPy、Pandas和Matplotlib）的实践经验。发展清洗、处理和可视化数据的能力。学习分析和解释数据所需的基本统计概念。开始使用Python进行机器学习。通过实践项目和练习为真实场景做好准备。立即报名，启动您的Python编程和数据科学之旅！
This comprehensive Python course is designed to take you from the basics of Python programming to advanced data science techniques, including data manipulation, visualization, statistics, and machine learning. Whether you're a complete beginner or looking to enhance your Python and data science skills, this course will provide you with practical knowledge through hands-on exercises and real-world examples.By the end of this course, you'll be able to write Python programs confidently, manipulate data using Pandas, create insightful visualizations using Matplotlib, and even build simple machine learning models. This course is ideal for aspiring developers, data analysts, or anyone who wants to dive deep into Python for data-driven tasks.Course ContentSection 1: Getting Started with PythonLecture 1: Data Types in PythonOverview of Python's data types, including integers, floats, strings, lists, tuples, sets, and dictionaries. Learn through practical examples and exercises, and explore common operations for each data type.Section 2: Python Basic ConstructsLecture 2: FunctionsDiscover how to define functions in Python, pass parameters, return values, and understand variable scope. Hands-on exercises will help you build and use functions in real-world scenarios.Section 3: Introduction to NumPyLecture 3: Performing Mathematical Functions Using NumPyLearn about NumPy arrays and their significance in scientific computing. Explore basic operations and mathematical functions through hands-on exercises.Section 4: NumPy AdvancedLecture 4: NumPy vs. ListUnderstand the key differences between NumPy arrays and Python lists through performance comparisons and practical examples.Lecture 5: SciPy IntroductionExplore SciPy and its ecosystem, focusing on its use in scientific computations with examples.Lecture 6: Sub-Package ClusterDive into SciPy's cluster sub-package and apply clustering techniques on real datasets.Section 5: Data Manipulation Using PandasLecture 7: Introduction to PandasGet introduced to Pandas and its powerful data structures, Series and DataFrame. Learn the importance of data manipulation.Lecture 8: DataFrame in PandasLearn how to create, manipulate, and filter data using Pandas DataFrames. Hands-on exercises will deepen your understanding.Lecture 9: Merge, Join, and ConcatenateMaster data combination techniques with merge, join, and concatenate functions.Lecture 10: Importing and Analyzing Data SetsDiscover methods to import and explore data from various sources.Lecture 11: Cleaning the Data SetLearn techniques for handling missing data, duplicates, and outliers.Lecture 12: Manipulating the Data SetExplore advanced manipulation techniques using apply, map, and groupby.Lecture 13: Visualizing the Data SetCreate insightful data visualizations with Pandas' built-in functions.Section 6: Data Visualization Using MatplotlibLecture 14: What Is Data Visualization?Understand the importance of data visualization and explore different types of visualizations and their use cases.Lecture 15: Introduction to MatplotlibGet hands-on with Matplotlib and learn basic plotting techniques.Lecture 16-22: Creating Different Types of PlotsMaster creating line, bar, scatter, histogram, box, violin, pie, doughnut, and area charts using step-by-step guides and practical exercises.Section 7: StatisticsLecture 23: What is Data?Learn the basics of data, its types, and data collection methods.Lecture 24: Introduction to StatisticsUnderstand core statistical concepts, including descriptive vs. inferential statistics.Lecture 25: SamplingDive into sampling methods and their importance in statistics.Lecture 26: ProbabilityLearn basic probability concepts and rules.Lecture 27: Probability DistributionExplore types of probability distributions and their applications.Lecture 28: Inferential StatisticsMaster hypothesis testing and confidence intervals for making data inferences.Section 8: Machine Learning Using PythonLecture 29: Types of Machine LearningGet an introduction to supervised, unsupervised, and reinforcement learning.Lecture 30: What Can You Do With Machine Learning?Explore real-world applications of machine learning across industries.Lecture 31: Machine Learning DemoFollow a step-by-step guide to building and evaluating a simple machine learning model.Why Enroll?By enrolling in this course, you will:Build a solid foundation in Python programming and data science.Gain hands-on experience with industry-standard libraries like NumPy, Pandas, and Matplotlib.Develop the skills to clean, manipulate, and visualize data.Learn essential statistical concepts to analyze and interpret data.Get started with machine learning using Python.Work through practical projects and exercises that will prepare you for real-world scenarios.Enroll now and kickstart your Python programming and data science journey!This course outline emphasizes the key learning outcomes, hands-on exercises, and structured progression that Udemy learners expect, providing both beginners and intermediates with the practical skills to advance in their Python and data science careers.
