本课程《用ChatGPT驱动的数据分析与可视化实战》是一门集人工智能、统计分析与Python实践于一体的完整训练营，专为希望借助AI提升数据处理与研究效率的职场人士与数据分析爱好者设计。课程涵盖从基础到高级分析场景，配有真实数据案例、Jupyter演示与ChatGPT操作示范，帮助你全面掌握“AI+数据分析”新技能，成为懂AI的高效数据分析专家！立即加入，开启AI赋能的专业分析之路。
你将学会：
- 使用 ChatGPT 自动完成数据分析任务，包括数据导入、预处理、缺失值分析、正态性检验等
- 编写并优化 Python 统计分析脚本（如t检验、ANOVA、回归等），快速生成APA风格报告
- 利用ChatGPT生成模拟数据集、建议统计方法、构建分析计划
- 结合 Jupyter Lab 与ChatGPT 实现可视化、相关性分析与高级建模（多元回归、Logistic回归等）
- 应用 Prompt Engineering 技巧与GPT对话高效驱动分析过程，节省分析时间、提升研究质量
适合人群：
- 有一定数据分析基础，想提升效率的研究人员、学生或数据工作者
- 希望掌握AI辅助分析流程，缩短学习曲线的Python新手
- 需要产出论文格式报告或APA风格输出的社会科学研究者
英文原版介绍
Published 4/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.43 GB | Duration: 8h 21m
Power UP Your Data Analysis Skills with Chat GPT. Analyse Complex Datasets, Create Stunning Visualizations with Chat GPT
What you’ll learn
Conduct AI Driven Data Analysis Using ChatGPT
Visualize Data and Identify Unique Patterns in Data Using ChatGPT
Write and Manage Python Codes for Advanced Statistical Analysis Using ChatGPT
Prepare Professional Reports and APA Style Write-Ups Using ChatGPT
Improve Research Productivity Using ChatGPT
Requirements
A computer or a similar device with an internet connection and a ChatGPT account
Description
Course DescriptionDiscover the power of data analysis and artificial intelligence with this unique course AI Driven Data Analysis Using Chat GPT. This course is designed to equip you with the knowledge and skills to leverage ChatGPT, one of the most advanced AI models developed by OpenAI, for in-depth data analysis. Whether you are a beginner curious about AI and data science or a seasoned professional looking to enhance your skills, this course provides a structured path from fundamental concepts to advanced applications.Learning OutcomesBy the end of this course, you will be able to:Understand the essentials of ChatGPT and its significance in data analysis.Navigate through different versions of ChatGPT and select the appropriate one for your needs.Set up and use Jupyter Lab for executing ChatGPT-powered data analysis tasks.Master prompt engineering to optimize interactions with ChatGPT for specific outputs.Conduct comprehensive data screenings, manage missing values, and understand different imputation methods using ChatGPT.Perform advanced statistical analysis, including hypothesis testing, ANOVA, and regression analysis, facilitated by ChatGPT.Generate and interpret data visualizations and statistical reports in APA format using ChatGPT.Develop and validate data-driven hypotheses, leveraging the AI’s capabilities to enhance accuracy and insights.Pre-requisitesThis course is accessible to learners with varying levels of experience. However, the following are recommended to ensure a smooth learning journey:Basic understanding of data analysis and statistics.Familiarity with Python programming is beneficial but not mandatory.Access to a computer capable of running Anaconda and Jupyter Lab.Unique FeaturesHands-On Learning: Each module includes practical exercises and projects, allowing you to apply concepts in real-time using ChatGPT.Comprehensive Coverage: From setting up your environment to advanced data analysis techniques, the course covers every aspect in detail.Expert Support: Gain insights and feedback from industry experts specializing in AI and data science.Flexible Learning: Access the course content at any time, and learn at your own pace with lifetime access to all resources.Course Content OverviewIntroductory Modules: Begin with an introduction to ChatGPT, its importance, and detailed guides on setting up your account and tools like Anaconda and Jupyter Lab.Data Handling: Learn to import and manipulate data efficiently in Jupyter Lab using ChatGPT, covering a range of file types and data structures.Prompt Engineering: Dive deep into prompt engineering, learning to craft prompts that guide ChatGPT to produce optimal outputs for various data analysis tasks.Statistical Analysis: Engage with modules on statistical tests, understanding and applying different methods such as t-tests, ANOVA, and various forms of regression analysis using both theoretical knowledge and ChatGPT’s computational power.Advanced Data Management: Tackle complex scenarios in data management, including missing value analysis and understanding data distribution properties.Final Projects: Apply everything you’ve learned in comprehensive projects that challenge you to use ChatGPT for real-world data analysis scenarios.This course not only enhances your analytical skills but also prepares you to be at the forefront of AI-assisted data science, making you a valuable asset in any data-driven industry. Join us to transform data into insights and AI understanding into practical expertise.
Overview
- Section 1: Introduction to Chat GPT
- Lecture 1 What is ChatGPT and Why You Must Know About It ?
- Lecture 2 Account Creation and Choosing Between Free and Paid Version of Chat GPT
- Lecture 3 Downloading and Installing Anaconda and Running Jupyter Lab
- Section 2: Working with Chat GPT and Jupyter Lab
- Lecture 4 Learning to Import an Excel Data File in Jupyter Lab with ChatGPT Code
- Lecture 5 Developing Familiarity with Jupyter Lab Note Book
- Section 3: Prompt Engineering
- Lecture 6 What is Prompt Engineering ?
- Lecture 7 Ten Principles of Effective Prompt Engineering Part 1
- Lecture 8 Understanding Temperature and Top-k Parameters
- Lecture 9 Understanding Pivoting
- Lecture 10 Depth Safety and Evaluation Principles of Prompt Engineering
- Lecture 11 Prompt Engineering Example Asking ChatGPT to Suggest Right Statistical Test
- Lecture 12 Prompt Engineering Example Using ChatGPT to Find an Impactful Research Idea
- Lecture 13 How to Use ChatGPT to Generate a Simulated Dataset for Factor Analsyis
- Lecture 14 Using ChatGPT for Manual Calculation of Factor Loadings
- Lecture 15 How to Use ChatGPT to Generate APA Style Tables
- Lecture 16 Using ChatGPT to generate APA style Write Up
- Lecture 17 Using ChatGPT to create a List of Major Statistical Test with Formula
- Section 4: Data Screening and Descriptive and Analysis in Chat GPT
- Lecture 18 Data Screening Using ChatGPT
- Lecture 19 Missing Value Analysis Methods Naive vs Imputation Methods
- Lecture 20 Understanding Listwise vs. Pairwise Deletion
- Lecture 21 Missing Value Analysis Imputation Methods
- Lecture 22 Missing Value Analysis Using ChatGPT Plus
- Lecture 23 Missing Value Analysis Using ChatGPT
- Lecture 24 Understanding Skewness
- Lecture 25 Calculating Skewness in ChatGPT Numerical and Visual Method
- Lecture 26 Calculating Pearson Bowley and Kelly’s Coefficients of Skewness Using Chat GPT +
- Lecture 27 Calculating Coefficients of Skewness in ChatGPT
- Lecture 28 Understanding Normality, Normal Distribution and Standard Normal Distribution
- Lecture 29 Historical Origin of Normal Distribution Gauss vs Laplace’s Contribution
- Lecture 30 Properties of Normal Distribution
- Lecture 31 Understanding Kolmogorov-Smirnov Test and Shapiro-Wilk Test
- Lecture 32 Perfomaity Normality Analysis in ChatGPT Plus and Reporting Result in APA format
- Lecture 33 Normality Test Using ChatGPT and Jupyter Lab
- Section 5: Data Analysis Plan Using ChatGPT
- Lecture 34 Introduction to Data Analaysis Steps
- Lecture 35 Role of Setting in Data Analysis Process
- Lecture 36 Steps Involved in Research Data Analysis From Raw
- Lecture 37 Understanding Differences Between Models vs. Theories
- Section 6: Descriptive Statistics Using ChatGPT
- Lecture 38 Descriptive Statistics Using ChatGPT
- Lecture 39 Understanidng Descriptive Statistics
- Lecture 40 Types of Measures of Central Tendency
- Lecture 41 Understanding Arithmetic Mean
- Lecture 42 Calculation of Arithmetic Mean using ChatGPT Plus
- Section 7: Analysis of Group Differences Using ChatGPT
- Lecture 43 Introduction to Analysis of Group Differences
- Lecture 44 Types of Group Difference tests
- Lecture 45 Assumptions of Parametric Tests
- Lecture 46 Understanding Statistical Formula of t-test
- Lecture 47 Understanding Data and Formulating Hypothesis
- Lecture 48 Calculation of t-test in ChatGPT Plus
- Lecture 49 Calculation of t-test using Chat GPT and Python
- Lecture 50 Paired Sample t-test Introduction and Formula
- Lecture 51 Understanding Data and Hypothesis Development for
- Lecture 52 Paired Sample t-Test in GPT Plus
- Lecture 53 Paired Sample t-test using CHatGPT and Python
- Lecture 54 Introduction to One-Way Anova
- Lecture 55 Theory and Calculation of One Way Anova
- Lecture 56 Understading Data and Developing Hypothesis
- Lecture 57 Conducting ANOVA Using ChatGPT Plus
- Lecture 58 Conducting ANOVA using ChatGPT and Python
- Section 8: Corrletaional Analysis Using ChatGPT
- Lecture 59 A Self-Introduction to Correlations
- Lecture 60 Calculation of Correlation Coefficient Using ChatGPT
- Lecture 61 Calculating Pearson Correlation using ChatGPT and P
- Section 9: Regression Analysis Using ChatGPT
- Lecture 62 Introduction of Regression Analysis Using ChatGPT
- Lecture 63 Types of Regression Linear Multiple Logistic
- Lecture 64 Types of Regression Polynomial Ridge and Lasso Reg
- Lecture 65 Types of Regression Elastic Net Quantile and Poisson
- Lecture 66 Assumptions of Linear Regression
- Lecture 67 Understanding Data and Formulating Hypothesis of Multiple Regression
- Lecture 68 Regression Analysis Using ChatGPT Plus
- Lecture 69 Regression Analysis Using GPT 3.5 and Python
This course is intended for working professionals looking to improve their productivity for research and data analysis.,It can also be useful for anyone looking to harness the power of AI for data analysis.
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