Python for Data Handling and Integration with OpenAI ChatGPT Training

• Some experience working with data.
• Algorithmic thinking

You will learn how to:

• Understand the fundamentals of programming with a focus on data handling.
• Learn to use Python and its IDEs for writing and running programs.
• Master data structures, flow control, and functions in Python.
• Gain proficiency in using Pandas for data manipulation and cleaning.
• Integrate and utilize OpenAI with Python for business applications.
• Apply model tuning, evaluation, and monitoring techniques.
• Explore practical examples and case studies demonstrating OpenAI in business contexts.

1. Introduction to Programming for Data Handling:

• Overview of programming concepts for data handling.
• Introduction to Python as a programming language for data analysis.
• Basic Python syntax and structure.

2. Introduction to Python and IDEs:

• Detailed introduction to Python, focusing on its strengths in data handling.
• Overview of various Integrated Development Environments (IDEs) compatible with Python.
• Setting up a Python development environment.

3. Data Structures, Flow Control, Functions & Basic Types:

• Exploration of Python's data structures (lists, tuples, dictionaries, sets).
• Understanding flow control (loops, conditionals).
• Introduction to functions and basic data types in Python.

4. Introduction to Pandas:

• Introducing Pandas library for data manipulation and analysis.
• Basic operations with Pandas DataFrames.
• Reading and writing data using Pandas.

5. Data Cleaning with Pandas:

• Techniques for cleaning and preprocessing data.
• Handling missing data, outliers, and data transformation.
• Practical examples of data cleaning.

6. Data Manipulation with Pandas:

• Advanced data manipulation techniques with Pandas.
• Aggregation, filtering, and transformation of datasets.
• Real-world examples of data manipulation.

7. Integrating OpenAI with Python:

• Overview of OpenAI's capabilities.
• How to integrate OpenAI APIs with Python.
• Practical applications and examples.

8. Tuning Your Model:

• Techniques for optimizing and tuning machine learning models.
• Practical tips for model tuning.

9. Applications of OpenAI in Business:

• Exploring various business applications of OpenAI.
• Case studies demonstrating the impact of OpenAI in different industries.
• Future trends in AI and business.

10. Practical Examples and Case Studies:

• A collection of real-world case studies and practical examples.
• Demonstrating the application of the concepts learned in previous modules.
• Insights into practical challenges and solutions in data handling and AI.