Python for Data Handling and Integration with OpenAI ChatGPT Training in France

This course provides an in-depth introduction to programming for data handling and analysis, using Python and its powerful libraries such as Pandas. It covers the basics of Python programming, data structures, flow control, functions, and progresses to advanced data manipulation techniques. The course also integrates OpenAI's capabilities with Python for various business applications.

Target Audience

The course is designed for individuals who have experience working with data using data analysis tools such as Excel and are looking to enhance their skill set in data handling and analysis through programming. This includes data analysts, business analysts, aspiring data scientists, and anyone interested in leveraging Python and OpenAI for data-driven solutions in business.

• 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.