Enhancing Your Productivity as a Data Scientist with Generative AI

This course teaches data scientists how to use generative AI to enhance their workflow in a practical, hands-on way. Along 20 interactive use cases, instructor Tobias Zwingmann shares practical AI-augmented techniques for each stage of the data science process, including problem formulation, data preparation, modeling, and deployment. Through hands-on exercises, learn AI tools to create and optimize models, interpret results, and communicate insights effectively. Check out this course to find out how you can apply generative AI methods to significantly increase your productivity and tackle more complex data science challenges.

Learning objectives
Implement at least 10 different use cases of generative AI across the data science lifecycle, from business understanding to model deployment and communication.
Demonstrate proficiency in using AI-assisted tools for data preparation by successfully cleaning text data, performing discretization, converting text to numeric data, and detecting outliers in a given dataset.
Create baseline predictive models for both classification and regression tasks using generative AI techniques, while also articulating the limitations of AI in predictive modeling.
Effectively use generative AI to augment model evaluation, including interpreting complex model outputs, identifying areas for model improvement, and generating synthetic test data for a given project.
Leverage generative AI to enhance project communication, as demonstrated by creating a data storyline, simulating a stakeholder interview, and generating comprehensive project documentation for a final project.