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Generative AI for Data Analysis and Engineering with ChatGPT
Rating: 4.6 out of 5(28 ratings)
338 students

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
Created byOak Academy, OAK Academy Team, Ali̇ CAVDAR
Last updated 6/2026
English

What you'll learn

  • Data analysis is the process of studying or manipulating a dataset to gain some sort of insight
  • 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
  • Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
  • Logistic Regression Algorithm
  • Cross Validation
  • ROC Curve and Area Under Curve (AUC)
  • ROC Curve and Area Under Curve (AUC)
  • Hyperparameter Tuning for Logistic Regression Model
  • Decision Tree Algorithm
  • Support Vector Machine Algorithm
  • Random Forest Algorithm
  • Generative AI is artificial intelligence (AI) that can create original content in response to a user's prompt or request

Course content

19 sections • 177 lectures • 21h 33m total length

  • The Main Prompt Source of The Course1:32

    What is Generative AI in data analysis and engineering?

    Generative AI refers to AI models that can create or synthesize data, code, or content. In data analysis and engineering, it helps automate tasks like data cleaning, generating synthetic datasets, and building predictive models.

  • Prompts34:53

    This lecture is a resource lecture. You will find all the prompts we asked ChatGPT throughout the course.


    How does ChatGPT assist data analysts and engineers?

    ChatGPT can generate Python or SQL code, suggest data transformations, explain complex algorithms, and help troubleshoot errors, making data workflows faster and more efficient.

  • Github Link

    In this lesson, we will provide a link to the project's Github repository, containing all the relevant code.


    Can Generative AI replace human data engineers?

    No, Generative AI is a tool that assists humans. It can automate repetitive tasks, provide suggestions, and speed up analysis, but human expertise is still required for problem-solving, validation, and decision-making.

  • Kaggle Link

    In this lesson, we will provide a link to the Kaggle page where the dataset and notebook can be accessed.


    What types of data tasks can Generative AI handle?

    Generative AI can handle data cleaning, transformation, visualization code generation, anomaly detection scripts, synthetic data creation, and even automated reporting.

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 Generative AI & ChatGPT
  • Curiosity for Artificial Intelligence and Data Science
  • Basic python knowledge
  • Nothing else! It’s just you, your computer and your ambition to get started today

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.


Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat.


A machine learning course teaches you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages.

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.


Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge.

If you are an aspiring data scientist, Kaggle is the best way to get started. Many companies will offer jobs to those who rank highly in their competitions. In fact, Kaggle may become your full-time job if you can hit one of their high rankings.


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



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 this course is for:

  • Anyone who wants to start learning AI & ChatGPT
  • 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
  • Data Analyst who want to apply generative AI tools to automate repetitive tasks, streamline data workflows, and generate insights.
  • Data Engineer who wants to optimize data pipelines and automate data-related tasks.
  • AI and Machine Learning Enthusiasts who want to deepen their understanding of how generative AI models, like ChatGPT, can be applied to real-world data tasks.
  • Business Analysts who wants to understand how generative AI can assist in generating business insights from raw data
  • Students or Beginners in Data Science who want to get familiar with cutting-edge AI tools and apply them to basic data analysis, engineering, or project automation.

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