Ai Driven Data Analysis Using Chatgpt

Ai Driven Data Analysis Using Chatgpt
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English

| Size: 2.43 GB[/center]
| 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.