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 Duration 14 hours

Course Outline

Introduction to ChatGPT in Data Science and Analytics

  • Defining ChatGPT and explaining its operational mechanics.
  • Reviewing the role ChatGPT plays in the fields of data science and analytics.

Exploring Data with ChatGPT

  • Applying ChatGPT to facilitate exploratory data analysis.
  • Formulating natural language queries to ChatGPT to uncover data insights.
  • Using ChatGPT to aid in data cleaning and preprocessing tasks.

Creating Insights via ChatGPT

  • Employing ChatGPT to identify patterns and trends within datasets.
  • Utilizing ChatGPT to support feature engineering and selection processes.
  • Assisting with hypothesis generation and testing using ChatGPT.

ChatGPT for Predictive Modeling

  • Integrating ChatGPT into predictive modeling workflows.
  • Generating predictions and forecasts with the help of ChatGPT.
  • Assisting in model selection and evaluation processes using ChatGPT.

ChatGPT for Natural Language Processing (NLP)

  • Leveraging ChatGPT for text analysis and sentiment assessment.
  • Extracting key information from unstructured text data.
  • Incorporating ChatGPT into NLP pipelines and related applications.

Best Practices for Using ChatGPT in Data Science

  • Fine-tuning ChatGPT to address specific data science requirements.
  • Addressing bias and fairness in AI-assisted analytics.
  • Monitoring performance and evaluating the results produced by ChatGPT.

Ethical Considerations for ChatGPT in Data Science

  • Ensuring responsible and transparent AI application in data science.
  • Mitigating risks and addressing ethical challenges linked to ChatGPT.
  • Understanding ethical implications when deploying AI models powered by ChatGPT.

Future Trends and Developments

  • Examining advancements in ChatGPT and the broader data science landscape.
  • Discussing the future impact of AI on data analytics.
  • Identifying opportunities for innovation and growth through ChatGPT in data science.

Summary and Next Steps

Requirements

  • Fundamental computer proficiency
  • Basic knowledge of data science concepts and common tools

Target Audience

  • Data scientists
  • Data analysts
  • Business analysts
  • Data engineers

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