Maîtrisez l’analyse de données avec ChatGPT: Formation complète

My biggest problem with ChatGPT is the inability to save data which you may be compiling. There should be a option to save a certain about of numerical data to reuse in your next analysis session vs having to reload each time

I had paid a guy from Pakistan to setup autogpt for me and it was a waste..all I wanted is to automate a way to browse webpages and convert them into PDFs..I wonder in this course can teach such..maybe I can do this myself 😅, would feel good not having to depend on anyone else like that.

Thank you Man, Congrats for this Nuggest Of Gold

1 question… Can we write off expenses related to professional appeaeance?

Hello, I am a student and have questions about the process. I normally drop unwanted features, rename the column, fix the data type, and then clean the dataset by handling the duplicate and missing values. I made sure the data was clean before performing EDA, and other statistics. Is this the correct order?

I'm constantly having to reupload the data. This makes going through this course a bit of pain when you can't go through the entire course in one sitting. Is this because I'm not using the Enterprise subscription? Or is this how it works regardless of subscription level.

ChatGPT for Data Analytics: A Comprehensive Course Breakdown

1⃣ Intro to ChatGPT (0:00 – 26:37)

* 0:00 Introduction: This course teaches how to use ChatGPT for data analytics, showcasing its time-saving potential and applications.
* 0:27 Course Overview: The course is divided into six chapters covering ChatGPT setup, prompting, plugins, data collection, and data analysis.
* 2:22 Options & Setup:
* Free vs. Plus: Free version is not suitable for the course; ChatGPT Plus is required for advanced features and costs $20 per month.
* Team vs. Enterprise: Enterprise edition offers secure handling of sensitive data, ideal for businesses, while Team offers reduced features for smaller teams.
* Setup: Signing up and upgrading to ChatGPT Plus is demonstrated.
* 6:59 Walkthrough: ChatGPT's interface is explored, covering the sidebar, chat history, settings, and access to custom-built models (GPTs).
* 9:00 Utilizing ChatGPT's Capabilities: Basic prompting techniques are demonstrated, emphasizing the importance of clear context and task definition for accurate results.
* 11:18 Personal Context Statements: Creating personal context statements to guide ChatGPT's responses is encouraged.
* 12:12 Updating ChatGPT's Knowledge: ChatGPT has a knowledge cut-off date (currently April 2023) and uses internet browsing for up-to-date information.
* 14:03 Mastering Prompting Techniques: Improving prompting with context and task considerations for better results.
* 16:33 Setting Up Custom Instructions: Setting up custom instructions for personalized and efficient use.
* 20:43 ChatGPT's Image Analysis: ChatGPT can analyze images, enabling interpretation of graphs and visualizations.

2⃣ Intro to Advanced Data Analysis (26:38 – 1:01:57)

* 26:38 Chapter Intro: This chapter focuses on using ChatGPT's Advanced Data Analysis plugin for data exploration, statistics, and machine learning predictions.
* 28:28 ADA Timeout Issues: A temporary bug where Advanced Data Analysis sessions might time out, requiring re-uploading of files.
* 30:00 ADA Intro: Comparison between ChatGPT with and without Advanced Data Analysis, highlighting its significance for data tasks.
* 36:02 Advanced Data Analysis Execution: ChatGPT's Advanced Data Analysis feature executes Python code directly in the chat interface, allowing users to verify calculations.
* 36:57 Versatility of Advanced Data Analysis: The feature can handle various tasks, including data processing, statistical analysis, predictive modeling, and custom queries.
* 38:03 File Type Flexibility: ChatGPT accepts various file types for data analysis, including CSV, Excel, Json, and more.
* 41:16 Descriptive & Exploratory Data Analysis: ChatGPT performs descriptive statistics and exploratory data analysis (EDA), providing insights through visualizations.
* 46:06 Importance of Data Cleanup: Data cleanup is crucial for accurate analysis, including removing spaces or renaming columns.
* 48:08 Complex Data Visualization: Visualizations, such as analyzing salary data in relation to job platforms, titles, and locations, offer deeper insights.
* 52:29 Machine Learning Predictions: ChatGPT builds machine learning models, like Random Forest, to predict data, such as yearly salaries.
* 55:36 Understanding Model Error Metrics: Root Mean Square Error (RMSE) evaluates the model's accuracy, indicating the average deviation from actual salaries.
* 56:30 Testing the Predictive Model: Testing the model with real-world scenarios demonstrates its practical application.
* 58:06 Model Performance Variability: Predictive accuracy varies across different roles, highlighting the importance of a diverse dataset.
* 59:42 Internet Access Limitation: Advanced Data Analysis cannot directly connect to online data sources, limiting its ability to analyze internet data.
* 1:00:24 File Size Limitation: There's a 512MB file size limit for uploads to ChatGPT.
* 1:01:58 Chapter Outro: The chapter summarizes the importance of using Advanced Data Analysis for ad-hoc analysis and highlights the potential for future advancements.

3⃣ Basic Analysis (1:03:27 – 2:09:04)

* 1:03:27 Chapter Intro: This chapter dives into basic visualizations and common statistics for those new to data analytics.
* 1:07:11 Core Visualization: Six common visualizations are explored: bar charts, line charts, pie charts, and scatter plots, with examples and use cases.
* 1:16:32 Statistical Visualizations: Histograms and box plots are examined for visualizing distributions and comparing data.
* 1:27:16 Visualization Best Practices: Recommendations for clutter-free visuals, focused attention, and effective communication with visualizations.
* 1:38:02 Basic Statistics: This section covers descriptive statistics for numerical and categorical data.
* 1:50:34 Four Types of Data Analytics: The four core types of data analytics are introduced: descriptive, diagnostic, predictive, and prescriptive.
* 1:54:43 Descriptive Analysis: Descriptive analysis focuses on understanding past data, using descriptive statistics and EDA.
* 1:59:06 Diagnostic Analysis: Diagnostic analysis explores the "why" behind observed trends, using drill-downs and further exploration of data.
* 2:04:57 Predictive Analysis: Predictive analysis uses past data to forecast future trends, often employing machine learning models.
* 2:09:05 Prescriptive Analysis: Prescriptive analysis provides recommendations for action based on predictions and insights.

4⃣ Advanced ChatGPT (2:12:56 – 2:47:32)

* 2:12:56 Chapter Intro: This chapter focuses on mitigating hallucinations, mastering prompting techniques, and understanding GPTs (customized ChatGPT models).
* 2:18:11 Hallucinations: Hallucinations, where ChatGPT provides incorrect information, are explored with real-world examples and explanations.
* 2:25:48 Prompting Best Practices: A six-part prompt formula is introduced for improving ChatGPT's responses, including task, context, exemplar, persona, format, and tone.
* 2:33:14 Custom Instructions: Custom instructions are demonstrated as a method for automating context, format, and tone preferences in ChatGPT.
* 2:39:01 GPTs: GPTs, custom-built ChatGPT models, are explored with examples and instructions on building GPTs for specific tasks.
* 2:47:13 Creating a Personalized GPT: The importance of refining and improving custom GPTs for optimal results is emphasized.

5⃣ Intro to Plugins (2:47:33 – 3:08:09)

* 2:47:33 Chapter Intro: This chapter introduces ChatGPT plugins for enhancing functionality and explores the benefits of using plugins for time-saving and automation.
* 2:53:16 Browse with Bing: Browsing the internet with ChatGPT is explored, highlighting its usefulness for retrieving up-to-date information.
* 2:58:27 Time-Saver Plugins: Various plugins are showcased for summarizing PDFs, web pages, articles, videos, and general knowledge.
* 3:03:27 Wolfram Plugin: The Wolfram plugin is explored for accessing a curated knowledge base of data, especially valuable for scientific and technical information.
* 3:08:17 DALL-E 3 Plugin for Image Generation: The DALL-E 3 plugin is introduced for generating images from text prompts, providing creative and visually appealing outputs.

6⃣ Data Collection (3:13:18 – 3:33:48)

* 3:13:18 Chapter Intro: This chapter focuses on data collection methods, including finding public datasets, web scraping, and utilizing APIs.
* 3:16:51 Public Data Sets: Resources for finding public datasets are discussed, including Kaggle, GitHub, Reddit, and Google Dataset Search.
* 3:20:58 Web Scraping Intro: Web scraping is introduced as a method for extracting information from websites, emphasizing legal and ethical considerations.
* 3:29:22 Using ChatGPT for Data Extraction: An example of legally extracting data from Glassdoor job postings is demonstrated using ChatGPT's Advanced Data Analysis plugin.
* 3:30:27 Advanced Concepts in Web Scraping: A deeper dive into the legality of web scraping is provided, highlighting a landmark case involving LinkedIn and data scraping.
* 3:32:19 Checking Website Legality for Scraping: Methods for verifying whether a website allows scraping are discussed, including checking robots.txt files and terms of service.
* 3:33:49 Course Wrap-up: The course concludes with a reminder to complete the end-of-course survey to receive a certificate and further promotes the use of ChatGPT for data analysis.

I used gemini-1.5-flash-latest to summarize the transcript.
Cost (if I didn't use the free tier): $0.0072
Input tokens: 87318
Output tokens: 2043

Great course Luke! I was wondering if whenever I upload a dataset file into a GPT chat, then I close the chat, then I reopen the chat, will the dataset file disappear? When I reopen the chat, and enter a new prompt to display a chart, it instead shows the python code. I would have to reupload the dataset file in order for the prompt to work

Thank you for sharing all this knowledge!

Amazing to find this available on Youtube

I'm highly convinced your the highlander 💯💯💯👀😂subscribed

Mon premier commentaire pour une video youtube. Ce cours est complet et grand merci pour avoir dedié autant de temps pour nous. Merci

Completed this course and it really inspired me to delve deeper into ChatGPT and data analysis. The course is really comprehensive, explains the topics well and has active examples which you can follow along with. I managed to get lost a couple of times, but learnt heaps finding my way back – really builds confidence playing with a experimental dataset!
One thing that confused me a little bit is that the dataset Luke uses for the course is a little older than the one that downloads, so the results are a little different.

What about data safety ? Doesn't chatgpt process later on that amount of data and feed itself with it ? Nothing is for free.