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 by: Oak Academy
Web & Mobile Development, IOS, Android, Ethical Hacking, IT

Created by: OAK Academy Team
instructor

Created by: Ali̇ CAVDAR
DATA SCIENTIST AND IT INSTRUCTOR

What Will I 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

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

Target audience

• 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.