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Course Outline

Introduction to ChatGPT in Data Science and Analytics

  • Defining ChatGPT and explaining its operational mechanics.
  • Providing an overview of ChatGPT’s function within data science and analytics.

Conducting Data Exploration with ChatGPT

  • Applying ChatGPT for exploratory data analysis.
  • Formulating natural language queries to extract data insights.
  • Using ChatGPT to assist with data cleaning and preprocessing.

Deriving Insights with ChatGPT

  • Identifying patterns and trends in datasets using ChatGPT.
  • Utilizing ChatGPT for feature engineering and selection processes.
  • Supporting hypothesis generation and testing via ChatGPT.

ChatGPT in Predictive Modeling

  • Integrating ChatGPT into predictive modeling workflows.
  • Creating predictions and forecasts with ChatGPT assistance.
  • Aiding in model selection and evaluation using ChatGPT.

ChatGPT for Natural Language Processing (NLP)

  • Leveraging ChatGPT for text analysis and sentiment assessment.
  • Extracting relevant information from unstructured text data.
  • Embedding ChatGPT into NLP pipelines and applications.

Best Practices for ChatGPT in Data Science and Analytics

  • Fine-tuning ChatGPT for specific data science objectives.
  • Managing bias and fairness in AI-assisted analytics.
  • Monitoring and assessing ChatGPT’s performance and outcomes.

Ethical Application of ChatGPT in Data Science and Analytics

  • Promoting responsible and transparent AI usage in data science.
  • Mitigating risks and addressing ethical challenges linked to ChatGPT.
  • Recognizing ethical implications when deploying AI models powered by ChatGPT.

Future Trends and Evolutions

  • Investigating advancements in ChatGPT and data science.
  • Analyzing the impact of AI on the future of data analytics.
  • Identifying opportunities for innovation and growth through ChatGPT in data science.

Summary and Subsequent Steps

Requirements

  • Fundamental computer proficiency
  • Basic understanding of data science concepts and associated tools

Target Audience

  • Data scientists
  • Data analysts
  • Business analysts
  • Data engineers
 14 Hours

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