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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-analytics-visualization-using-python-with-project/
课程评论：没有评论
Coursera 上的“使用 Python 进行数据分析与可视化（含项目）”课程是一门快节奏、高强度的速成课，适合已有编程基础的学习者，也欢迎零基础的初学者。本课程旨在帮助您快速巩固 Python 技能，深入学习数据分析与可视化，特别适合用于考前复习或快速回顾关键概念。 **课程内容涵盖：** * **商业与数据导论：** 了解数据在商业中的作用，并概览课程结构。 * **Python 基础与 Jupyter Notebook：** 快速回顾 Python 语法、数据类型，并学会使用 Jupyter Notebook 进行交互式分析。 * **运算符与条件语句：** 掌握 Python 中的各类运算符和条件语句。 * **循环与函数：** 复习循环和函数的用法，提升代码模块化能力。 * **面向对象编程（OOP）与 NumPy：** 了解 OOP 原则，并快速上手 NumPy 进行数组操作和数值计算。 * **Pandas 库与数据处理：** 学习 Pandas 的核心概念，包括 Series 和 DataFrame，高效处理数据。 * **文件处理与数据导入：** 掌握文件处理技巧，理解结构化与半结构化数据，并学会导入 JSON 和 Excel 文件。 * **数据清洗与预处理：** 复习数据清洗策略和 Pandas 方法。 * **探索性数据分析（EDA）：** 快速回顾 EDA 技术，并通过实践 sessions 掌握数据探索与洞察。 * **高级主题：** 学习数据收集技术，通过实际 API 操作巩固，并快速复习 NumPy 在线性代数中的应用。 * **毕业项目：** 完成一项“学生安置预测”的实际项目，运用所学技能解决真实问题。 **课程亮点：** * 专为有编程基础的学习者设计，零基础亦可报名。 * 速成课程，注重快速理解和应用。 * 适合考前复习和巩固。 * 提供密集的实践学习体验，聚焦真实场景。 立即报名，开启您在 Python 商业数据分析领域的加速学习之旅，体验学以致用的乐趣！
Welcome to the Data Analysis course. a fast-paced and intensive crash course tailored for individuals with some prior programming experience. This course is specifically designed for learners looking to quickly refresh their Python skills and delve into the world of data analysis and Visualization, making it an ideal choice for those seeking rapid revision for exams or a swift recap of essential concepts.Module 1: Introduction to Business and Data1.1 Overview: A rapid introduction to the role of data in business and a concise overview of the course curriculum.1.2 Key Concepts: Swiftly grasp key concepts in business data analysis, setting the stage for the rest of the course.1.3 Python Introduction: Quickly refresh your Python knowledge, emphasizing key aspects relevant to business data analysis.Module 2: Python Basics and Jupyter Notebooks2.1.1-2.1.3 Python Programming Basics: A condensed exploration of Python fundamentals, covering syntax, data types, and basic programming concepts.2.2 Understanding Jupyter Notebook: Rapidly familiarize yourself with Jupyter Notebooks for interactive and collaborative data analysis.Module 3: Operators and Conditionals3.1 Operators in Python: Swiftly navigate through the various operators for efficient data manipulation.3.2 Conditionals in Python: Quickly review the use of conditional statements to control program flow.Module 4: Loops and Functions4.1 Loops in Python: Efficiently revisit the use of loops for iterative processes.4.2 Functions in Python: Rapidly refresh your understanding of creating and using functions for modular code.Module 5: Object-Oriented Programming (OOP) and NumPy5.1 Object-Oriented Programming: A brisk exploration of OOP principles for structured code.5.2.1-5.2.2 Arrays in Python and Numpy Overview: Swiftly introduce NumPy for handling arrays and numerical operations.Module 6: pandas Library and Data Manipulation6.1-6.3 Introduction to pandas, pandas Series, and Working with DataFrames: Quickly grasp the essentials of pandas for efficient data manipulation.Module 7: Working with Files and Data Importing7.1-7.3 File Handling, Structured vs. Semi-Structured Data, and Importing JSON and Excel files: Swiftly understand file handling, data structures, and data importing techniques.Module 8: Data Cleaning and Preprocessing8.1-8.2 Data Cleaning Techniques, pandas Methods, and Operations: Efficiently review strategies for cleaning and preprocessing data using pandas.Module 9: Exploratory Data Analysis (EDA)9.1-9.2 Exploratory Data Analysis (EDA) and EDA Practical Session: Quickly revisit techniques for exploring and visualizing data to gain insights.Module 10: Advanced Topics10.1-10.2 Data Gathering Techniques and Practical Exercises with Real-world APIs: Swiftly explore advanced data collection methods and apply them through practical exercises.10.3 Linear Algebra and NumPy: A quick revision of linear algebra concepts and their application using NumPy.Module 11: Capstone Project11. Project - Student Placement Prediction: Apply your refreshed skills to a real-world problem with a focus on quick application and practical understanding.Course Highlights:Ideal for learners with prior programming experience, immediate beginners can also enroll.A crash course designed for quick understanding and application.Perfect for rapid revision and exam preparation.Intensive, hands-on learning with a focus on practical scenarios.Enrol now for an accelerated journey into Python for Business Data Analysis, where swift learning meets practical application!
