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Generative AI for Data Analysis and Engineering with ChatGPT

ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Provided by Oak Academy

Summary

Price
£49 inc VAT
Study method
Online, On Demand What's this?
Duration
11.9 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Additional info
  • Tutor is available to students

1 student purchased this course

Add to basket or enquire

Overview

Hi there,

Welcome to "Generative AI for Data Analysis and Engineering with ChatGPT" course.
ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Artificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age.

In today's data-driven world, the ability to analyze data, draw meaningful insights, and apply machine learning algorithms is more crucial than ever. This course is designed to guide you through every step of that journey, from the basics of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.

What You’ll Learn:

  • Exploratory Data Analysis (EDA): Master the techniques for analyzing and visualizing data, detecting trends, and preparing data for modeling.

  • Machine Learning Algorithms: Implement algorithms like Logistic Regression, Decision Trees, and Random Forest, and understand when and how to use them.

  • ChatGPT-4o Integration: Leverage the AI capabilities of ChatGPT-4o to automate workflows, generate code, and improve data insights.

  • Real-World Applications: Apply the knowledge gained to solve complex problems and make data-driven decisions in industries such as finance, healthcare, and technology.

  • Next-Gen AI Techniques: Explore advanced techniques that combine AI with machine learning, pushing the boundaries of data analysis.


Why This Course Stands Out:

Unlike traditional data science courses, this course blends theory with practice. You won’t just learn how to perform data analysis or build machine learning models—you’ll also apply these skills in real-world scenarios with guidance from ChatGPT-4o. The hands-on projects ensure that by the end of the course, you can confidently take on any data challenge in your professional career.


In this course, you will Learn:

  • Big News: Introducing ChatGPT-4o

  • How to Use ChatGPT-4o?

  • Chronological Development of ChatGPT

  • What Are the Capabilities of ChatGPT-4o?

  • As an App: ChatGPT

  • Voice Communication with ChatGPT-4o

  • Instant Translation in 50+ Languages

  • Interview Preparation with ChatGPT-4o

  • Visual Commentary with ChatGPT-4o

  • ChatGPT for Generative AI Introduction

  • Accessing the Dataset

  • First Task: Field Knowledge

  • Continuing with Field Knowledge

  • Loading the Dataset and Understanding Variables

  • Delving into the Details of Variables

  • Let's Perform the First Analysis

  • Updating Variable Names

  • Examining Missing Values

  • Examining Unique Values

  • Examining Statistics of Variables

  • Exploratory Data Analysis (EDA)

  • Categorical Variables (Analysis with Pie Chart)

  • Importance of Bivariate Analysis in Data Science

  • Numerical Variables vs Target Variable

  • Categoric Variables vs Target Variable

  • Correlation Between Numerical and Categorical Variables and the Target Variable

  • Examining Numeric Variables Among Themselves

  • Numerical Variables - Categorical Variables

  • Numerical Variables - Categorical Variables with Swarm Plot

  • Relationships between variables (Analysis with Heatmap)

  • Preparation for Modeling

  • Dropping Columns with Low Correlation

  • Struggling Outliers

  • Visualizing Outliers

  • Dealing with Outliers

  • Determining Distributions

  • Determining Distributions of Numeric Variables

  • Applying One Hot Encoding Method to Categorical Variables

  • Feature Scaling with the RobustScaler Method for Machine Learning Algorithms

  • Separating Data into Test and Training Set

  • Logistic Regression Algorithm

  • Cross Validation

  • ROC Curve and Area Under Curve (AUC)

  • Hyperparameter Optimization (with GridSearchCV)

  • Hyperparameter Tuning for Logistic Regression Model

  • Decision Tree Algorithm

  • Support Vector Machine Algorithm

  • Random Forest Algorithm

Summary

  • Beginners who want a structured, comprehensive introduction to data analysis and machine learning.

  • Data enthusiasts looking to enhance their AI-driven analysis and modeling skills.

  • Professionals who want to integrate AI tools like ChatGPT-4o into their data workflows.

  • Anyone interested in mastering the art of data analysis, machine learning, and next-generation AI techniques.

See you in the "Generative AI for Data Analysis and Engineering with ChatGPT" course.
ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Certificates

Reed Courses Certificate of Completion

Digital certificate - Included

Will be downloadable when all lectures have been completed.

Curriculum

12
sections
101
lectures
11h 55m
total
    • 1: The Main Prompt Source of The Course 01:00
    • 2: Prompts - 1 04:00
    • 3: Prompts - 2 05:00
    • 4: Prompts - 3 05:00
    • 5: Prompts - 4 05:00
    • 6: Prompts - 5 05:00
    • 7: Prompts - 6 04:00
    • 8: Prompts - 7 03:00
    • 9: Github and Kaggle Link 01:00
    • 10: How to Use ChatGPT-4o ? 05:52
    • 11: Chronological Development of ChatGPT 05:20
    • 12: What Are the Capabilities of ChatGPT-4o 04:32
    • 13: As an App- ChatGPT 03:18
    • 14: Voice Communication with ChatGPT-4o 04:50
    • 15: Instant Translation in 50+ Languages 03:03
    • 16: Interview Preparation with ChatGPT-4o 18:06
    • 17: Visual Commentary with ChatGPT-4o 04:19
    • 18: ChatGPT for Generative AI Introduction 04:21
    • 19: Accessing the Dataset 01:35
    • 20: First Task: Field Knowledge 11:12
    • 21: Continuing with Field Knowledge 05:42
    • 22: Loading the Dataset and Understanding Variables 07:55
    • 23: Delving into the Details of Variables 05:35
    • 24: Let's Perform the First Analysis 06:37
    • 25: Updating Variable Names 06:19
    • 26: Examining Missing Values 06:07
    • 27: Examining Unique Values 14:12
    • 28: Examining Statistics of Variables Lesson 1 15:15
    • 29: Examining Statistics of Variables Lesson 2 13:11
    • 30: Examining Statistics of Variables Lesson 3 09:19
    • 31: 15 Exploratory Data Analysis(EDA) 09:59
    • 32: Categorical Variables(Analysis with Pie Chart) Lesson 1 10:40
    • 33: Categorical Variables(Analysis with Pie Chart) Lesson 2 09:35
    • 34: Categorical Variables(Analysis with Pie Chart) Lesson 3 06:52
    • 35: Categorical Variables(Analysis with Pie Chart) Lesson 4 16:44
    • 36: Categorical Variables(Analysis with Pie Chart) Lesson 5 11:19
    • 37: Importance of Bivariate Analysis in Data Science 07:16
    • 38: Numerical Variables vs Target Variable Lesson 1 06:42
    • 39: Numerical Variables vs Target Variable Lesson 2 09:56
    • 40: Numerical Variables vs Target Variable Lesson 3 08:24
    • 41: Numerical Variables vs Target Variable Lesson 4 03:36
    • 42: Categoric Variables vs Target Variable Lesson 1 03:37
    • 43: Categoric Variables vs Target Variable Lesson 2 05:31
    • 44: Categoric Variables vs Target Variable Lesson 3 05:21
    • 45: Categoric Variables vs Target Variable Lesson 4 04:45
    • 46: Categoric Variables vs Target Variable Lesson 5 05:51
    • 47: Correlation Between Numerical and Categorical Variables and the Target Variab 11:42
    • 48: Correlation Between Numerical and Categorical Variables and the Target Variab 07:56
    • 49: Examining Numeric Variables Among Themselves Lesson 1 06:11
    • 50: Examining Numeric Variables Among Themselves Lesson 2 06:55
    • 51: Numerical Variables - Categorical Variables Lesson 1 18:12
    • 52: Numerical Variables - Categorical Variables Lesson 2 06:01
    • 53: Numerical Variables - Categorical Variables Lesson 3 05:21
    • 54: Numerical Variables - Categorical Variables Lesson 4 05:17
    • 55: Numerical Variables - Categorical Variables Lesson 5 05:47
    • 56: Numerical Variables - Categorical Variables with Swarm Plot Lesson 1 12:33
    • 57: Numerical Variables - Categorical Variables with Swarm Plot Lesson 2 07:19
    • 58: Numerical Variables - Categorical Variables with Swarm Plot Lesson 3 07:00
    • 59: Numerical Variables - Categorical Variables with Swarm Plot Lesson 4 04:35
    • 60: Numerical Variables - Categorical Variables with Swarm Plot Lesson 5 04:31
    • 61: Numerical Variables - Categorical Variables with Swarm Plot Lesson 6 08:48
    • 62: Relationships between variables (Analysis with Heatmap) 08:07
    • 63: 47 Relationships between variables (Analysis with Heatmap) Lesson 2 14:44
    • 64: Preparation for Modeling 05:23
    • 65: Dropping Columns with Low Correlation 05:26
    • 66: Struggling Outliers 09:50
    • 67: Visualizing Outliers Lesson 06:30
    • 68: Visualizing Outliers Lesson 2 04:33
    • 69: Visualizing Outliers Lesson 3 03:51
    • 70: 54 Dealing with Outliers Lesson 1 09:24
    • 71: 55 Dealing with Outliers Lesson 2 13:50
    • 72: 56 Dealing with Outliers Lesson 3 06:02
    • 73: 57 Dealing with Outliers Lesson 4 06:38
    • 74: 58 Dealing with Outliers Lesson 5 10:03
    • 75: 59 Determining Distributions 11:24
    • 76: 60 Determining Distributions of Numeric Variables Lesson 1 06:26
    • 77: Determining Distributions of Numeric Variables Lesson 2 04:05
    • 78: Determining Distributions of Numeric Variables Lesson 3 04:30
    • 79: Determining Distributions of Numeric Variables Lesson 4 08:20
    • 80: Determining Distributions of Numeric Variables Lesson 5 06:53
    • 81: Applying One Hot Encoding Method to Categorical Variables Lesson 1 05:19
    • 82: Applying One Hot Encoding Method to Categorical Variables Lesson 2 02:31
    • 83: Feature Scaling with the RobustScaler Method for Machine Learning Algorithms 03:44
    • 84: Separating Data into Test and Training Set 04:00
    • 85: Logistic Regression Algorithm Lesson 1 06:13
    • 86: Logistic Regression Algorithm Lesson 2 11:15
    • 87: Cross Validation 08:44
    • 88: ROC Curve and Area Under Curve (AUC) 06:56
    • 89: ROC Curve and Area Under Curve (AUC) Lesson 2 05:48
    • 90: Hyperparameter Optimization (with GridSearchCV) 07:33
    • 91: Hyperparameter Tuning for Logistic Regression Model 08:28
    • 92: Decision Tree Algorithm Lesson 1 05:50
    • 93: Decision Tree Algorithm Lesson 2 06:55
    • 94: 78 Support Vector Machine Algorithm Lesson 1 05:34
    • 95: Support Vector Machine Algorithm Lesson 2 06:23
    • 96: Random Forest Algorithm Lesson 1 06:09
    • 97: Random Forest Algorithm Lesson 2 03:37
    • 98: Random Forest Algorithm Lesson 3 05:03
    • 99: Random Forest Algorithm Lesson 4 05:43
    • 100: Project Conclusion 10:35
    • 101: Suggestions and Closing 08:07

Course media

Description

Hi there,

Welcome to "Generative AI for Data Analysis and Engineering with ChatGPT" course.
ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Artificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age.

In today's data-driven world, the ability to analyze data, draw meaningful insights, and apply machine learning algorithms is more crucial than ever. This course is designed to guide you through every step of that journey, from the basics of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.


What This Course Offers:

In this course, you will gain a deep understanding of the entire data analysis and machine learning pipeline. Whether you are new to the field or looking to expand your existing knowledge, our hands-on approach will equip you with the skills you need to tackle real-world data challenges.

You’ll begin by diving into the fundamentals of EDA, where you’ll learn how to explore, visualize, and interpret datasets. With step-by-step guidance, you’ll master techniques to clean, transform, and analyze data to uncover trends, patterns, and outliers—key steps before jumping into predictive modeling.


Why ChatGPT-4o?

This course uniquely integrates ChatGPT-4o, the next-gen AI tool, to assist you throughout your learning journey. ChatGPT-4o will enhance your productivity by automating tasks, helping with code generation, answering queries, and offering suggestions for better analysis and model optimization. You’ll see how this cutting-edge AI transforms data analysis workflows and unlocks new levels of efficiency and creativity.


Mastering Machine Learning:

Once your foundation in EDA is solid, the course will guide you through advanced machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and more. You’ll learn not only how these algorithms work but also how to implement and optimize them using real-world datasets. By the end of the course, you’ll be proficient in selecting the right models, fine-tuning hyperparameters, and evaluating model performance with confidence.


What You’ll Learn:

  • Exploratory Data Analysis (EDA): Master the techniques for analyzing and visualizing data, detecting trends, and preparing data for modeling.

  • Machine Learning Algorithms: Implement algorithms like Logistic Regression, Decision Trees, and Random Forest, and understand when and how to use them.

  • ChatGPT-4o Integration: Leverage the AI capabilities of ChatGPT-4o to automate workflows, generate code, and improve data insights.

  • Real-World Applications: Apply the knowledge gained to solve complex problems and make data-driven decisions in industries such as finance, healthcare, and technology.

  • Next-Gen AI Techniques: Explore advanced techniques that combine AI with machine learning, pushing the boundaries of data analysis.


Why This Course Stands Out:

Unlike traditional data science courses, this course blends theory with practice. You won’t just learn how to perform data analysis or build machine learning models—you’ll also apply these skills in real-world scenarios with guidance from ChatGPT-4o. The hands-on projects ensure that by the end of the course, you can confidently take on any data challenge in your professional career.


In this course, you will Learn:

  • Big News: Introducing ChatGPT-4o

  • How to Use ChatGPT-4o?

  • Chronological Development of ChatGPT

  • What Are the Capabilities of ChatGPT-4o?

  • As an App: ChatGPT

  • Voice Communication with ChatGPT-4o

  • Instant Translation in 50+ Languages

  • Interview Preparation with ChatGPT-4o

  • Visual Commentary with ChatGPT-4o

  • ChatGPT for Generative AI Introduction

  • Accessing the Dataset

  • First Task: Field Knowledge

  • Continuing with Field Knowledge

  • Loading the Dataset and Understanding Variables

  • Delving into the Details of Variables

  • Let's Perform the First Analysis

  • Updating Variable Names

  • Examining Missing Values

  • Examining Unique Values

  • Examining Statistics of Variables

  • Exploratory Data Analysis (EDA)

  • Categorical Variables (Analysis with Pie Chart)

  • Importance of Bivariate Analysis in Data Science

  • Numerical Variables vs Target Variable

  • Categoric Variables vs Target Variable

  • Correlation Between Numerical and Categorical Variables and the Target Variable

  • Examining Numeric Variables Among Themselves

  • Numerical Variables - Categorical Variables

  • Numerical Variables - Categorical Variables with Swarm Plot

  • Relationships between variables (Analysis with Heatmap)

  • Preparation for Modeling

  • Dropping Columns with Low Correlation

  • Struggling Outliers

  • Visualizing Outliers

  • Dealing with Outliers

  • Determining Distributions

  • Determining Distributions of Numeric Variables

  • Applying One Hot Encoding Method to Categorical Variables

  • Feature Scaling with the RobustScaler Method for Machine Learning Algorithms

  • Separating Data into Test and Training Set

  • Logistic Regression Algorithm

  • Cross Validation

  • ROC Curve and Area Under Curve (AUC)

  • Hyperparameter Optimization (with GridSearchCV)

  • Hyperparameter Tuning for Logistic Regression Model

  • Decision Tree Algorithm

  • Support Vector Machine Algorithm

  • Random Forest Algorithm

Summary

  • Beginners who want a structured, comprehensive introduction to data analysis and machine learning.

  • Data enthusiasts looking to enhance their AI-driven analysis and modeling skills.

  • Professionals who want to integrate AI tools like ChatGPT-4o into their data workflows.

  • Anyone interested in mastering the art of data analysis, machine learning, and next-generation AI techniques.

What You’ll Gain:

By the end of this course, you will have a robust toolkit that enables you to:

  • Transform raw data into actionable insights with EDA.

  • Build, evaluate, and fine-tune machine learning models with confidence.

  • Use ChatGPT-4o to streamline data analysis, automate repetitive tasks, and generate faster results.

  • Apply advanced AI techniques to tackle industry-level problems and make data-driven decisions.

This course is your gateway to mastering data analysis, machine learning, and AI, and it’s designed to provide you with both the theoretical knowledge and practical skills needed to succeed in today’s data-centric world.

Join us on this complete journey and unlock the full potential of data with ChatGPT-4o and advanced machine learning algorithms. Let’s get started!

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for Download

Dive in now!

We offer full support, answering any questions.

See you in the "Generative AI for Data Analysis and Engineering with ChatGPT" course.
ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Who is this course for?

  • Anyone who wants to start learning AI & ChatGPT
  • Anyone who wants to start data science
  • Anyone who needs a complete guide on how to start and continue their career with AI & Prompt Engineering
  • And also, who want to learn how to develop Prompt Engineering

Requirements

  • A working computer (Windows, Mac, or Linux)
  • Motivation to learn the the second largest number of job postings relative AI among all others
  • Desire to learn AI & ChatGPT
  • Curiosity for Artificial Intelligence
  • Curiosity for Data Science
  • Nothing else! It’s just you, your computer and your ambition to get started today
  • Basic python knowledge

Questions and answers

Currently there are no Q&As for this course. Be the first to ask a question.

Reviews

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Provider

Oak Academy

At OAK Academy, we are the tech experts who have been in the sector for years and years. We are deeply rooted in the tech world. As insiders we know the tech industry’s biggest problem is the “tech skills gap” and here is our solution.

OAK Academy will be the bridge between the tech industry and people who

-are planning a new career

-are thinking career transformation

-want career shift or reinvention,

-have the desire to learn new hobbies at their own pace

We help people of this generation gain the skill to fill these jobs and to enjoy a happier, and a more fulfilling career prospect. This is what motivates us every day.

We specialize in critical areas like cybersecurity, coding, IT, game development, app monetization, amazon fba, web and mobile development technologies. Thanks to our practical alignment, we are able to constantly translate industry insights into the most in-demand and up-to-date courses.

OAK Academy will provide you the information and support you need to move through your new career journey, with confidence and ease.

Our courses are for everyone. Whether you are someone who has never programmed before, or an existing programmer seeking to learn another language, or even someone looking to switch careers we are here.

OAK Academy here to transforms passionate, enthusiastic people to reach their dream job positions.

If you need help or if you have any questions, please do not hesitate to contact our team.

View Oak Academy profile

FAQs

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