ChatGPT for Data Science and Data Analysis in Python

Overview

Learn to Leverage AI to Fast-Track Your Data Science Project Execution, Data Analysis, Data Visualization and Reporting

Syllabus

- Introduction to ChatGPT and AI in Data Science -- Overview of AI and ChatGPT -- Applications of ChatGPT in data science - Setting Up the Environment -- Python installation and configuration -- Installing necessary libraries (OpenAI, Pandas, NumPy, etc.) -- Creating and managing API keys - Fundamentals of Data Analysis in Python -- Introduction to Pandas and NumPy -- Data cleaning and preprocessing -- Exploratory data analysis (EDA) techniques - Integrating ChatGPT with Data Science Workflows -- Using ChatGPT for data exploration -- Automating data cleaning and preprocessing with ChatGPT -- Generating hypothesis and insights using ChatGPT - Advanced Data Processing with ChatGPT -- Pattern recognition and anomaly detection with AI -- NLP techniques for unstructured data analysis -- ChatGPT-driven data summarization and report generation - Case Studies and Real-World Applications -- Automating customer feedback analysis -- ChatGPT for predictive analytics and forecasting -- Enhancing data visualization with AI-generated insights - Best Practices and Ethical Considerations -- Maximizing efficiency with ChatGPT -- Addressing bias and ensuring data privacy -- Evaluating ChatGPT outputs critically - Practical Projects and Hands-On Exercises -- Data analysis project using ChatGPT -- Interactive exercises with datasets and ChatGPT -- Capstone project - Future Trends in AI and Data Science -- Emerging technologies in AI and its impact on data science -- The evolving role of AI in data analysis - Course Summary and Conclusion -- Key takeaways -- Additional resources for continued learning

Taught by

Tony Simonovsky, Ligency Team and SuperDataScience Team

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