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Course Outline

Getting Started with ChatGPT in Data Science and Analytics

  • Explaining what ChatGPT is and its underlying mechanisms.
  • A broad perspective on ChatGPT's contribution to data science and analytics.

Conducting Data Exploration with ChatGPT

  • Utilizing ChatGPT to perform exploratory data analysis.
  • Querying ChatGPT using natural language to extract data insights.
  • Using ChatGPT to aid in data cleaning and preprocessing steps.

Deriving Insights with ChatGPT

  • Employing ChatGPT to identify patterns and trends within datasets.
  • Using ChatGPT to support feature engineering and selection processes.
  • Leveraging ChatGPT to assist in generating and testing hypotheses.

Applying ChatGPT to Predictive Modeling

  • Integrating ChatGPT into predictive modeling workflows.
  • Creating predictions and forecasts with the assistance of ChatGPT.
  • Using ChatGPT to help select and evaluate models.

ChatGPT for Natural Language Processing (NLP)

  • Applying ChatGPT for text analysis and sentiment evaluation.
  • Pulling significant information from unstructured text data.
  • Embedding ChatGPT into NLP pipelines and practical applications.

Best Practices for Using ChatGPT in Data Science and Analytics

  • Fine-tuning ChatGPT for specialized data science objectives.
  • Navigating bias and fairness issues in AI-assisted analytics.
  • Tracking and assessing the performance and output of ChatGPT.

Ethical Considerations for ChatGPT in Data Science and Analytics

  • Guaranteeing responsible and transparent AI usage in data science.
  • Reducing risks and addressing ethical complexities related to ChatGPT.
  • Grasping the ethical implications of deploying AI models driven by ChatGPT.

Future Trends and Advancements

  • Investigating recent developments in ChatGPT and the broader data science field.
  • Considering the impact of AI on the future landscape of data analytics.
  • Identifying opportunities for innovation and expansion using ChatGPT in data science.

Recap and Forward-Looking Steps

Requirements

  • Fundamental computer literacy
  • General understanding of data science methodologies and associated tools

Target Audience

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

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