Data Analysis 3 in 1: Excel, Python and ChatGPT

本课程《数据分析全能指南：Excel + Python + ChatGPT 实战解析》是一门系统性强、实用性高的综合课程，带你掌握从数据清洗到统计分析、从可视化到实际问题解决的完整数据分析流程。适合零编程基础、有志于提升数据分析力的学习者，帮助你打通技能壁垒，成为能灵活运用多种工具的全能型分析人才。

你将深入学习：
Excel 技能：数据清洗、处理缺失值与异常值、使用函数与图表进行分析、构建交互式仪表板（PivotTable/PivotChart）；
Python 编程：掌握语法、数据结构与控制流程，灵活处理大规模数据，扩展Excel无法处理的分析任务；
统计分析：掌握描述性统计、推论统计与假设检验，并能准确解读和传达分析结果；
ChatGPT 辅助：借助AI快速生成代码、优化逻辑、加快分析效率；
60+ 实战题目：结合真实业务场景，锻炼问题解决能力和数据驱动决策的能力。

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教程名称：Data Analysis 3 in 1: Excel, Python and ChatGPT

下载连结：https://www.nidown.com/chatgpt-297132.html

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查看详情：https://www.kkmac.com/go/chatgpt

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英文原版介绍

Master Complete Data Analysis Workflow. Unleash Two Widely Used Tools – Excel and Python. Empower Yourself with ChatGPT.

What you’ll learn:

Learn Python’s syntax, data types, variables, and operators to construct simple programs and execute basic functions.
To manage program flow, use loops and conditional statements like if, elif, and else.
Learn to use Python lists, dictionaries, tuples, and sets.
Learn to access, change, and manipulate these structures for various programming needs.
Understand and apply techniques for cleaning and preparing raw data in Excel.
Learn to identify and handle missing data, outliers, and inconsistencies.
Utilize Excel functions and tools for data validation and transformation.
Explore fundamental statistical concepts and their application in Excel.
Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools.
Understand how to interpret and communicate statistical results effectively.
Design and build interactive dashboards in Excel for effective data visualization.
Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards.
Explore various data visualization techniques available in Excel, including charts, graphs.
Learn and apply the data analysis methodology, from data cleaning to hypothesis testing, in real-world applications.
Increase your critical thinking and problem-solving skills for data analysis, decision-making, and recommendation.
Use value counts, percentage, group by, pivot tables, correlation, and regression professionally and realistically.
Solve over 60+ real-world data analytical questions to practice applying data analysis to various circumstances.
Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions.