Generative AI: Elevate Your Data Science Career

IBM via Coursera

Coursera based on 168 ratings

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Overview

Generative AI is now mainstream. Boost your career with a course that features leading-edge, in-demand, generative AI skills tuned to the needs of data scientists. This course is suitable for existing and aspiring data scientists, data professionals, analysts, and engineers. The course addresses real-world data science problems data scientists encounter across multiple industries with data generation, data augmentation, and feature engineering. Gain skills you can immediately put to use implementing generative AI models and techniques that address these...

Syllabus

Data Science and Generative AI
- In this module, you will explore the role of generative AI in data science. Lesson 1 introduces you to generative AI and how it can serve various purposes in the hands of data scientists. You will learn about the four common types of generative AI models and their impact and applications across diverse industries. Lesson 2 will cover how data scientists can leverage generative AI in the data science lifecycle. You will learn how data scientists can effectively use generative AI to perform data generation, data preparation, data querying, and data augmentation. You will also learn about data preparation and querying challenges and how generative AI models can tackle these challenges.
Use of Generative AI for Data Science
- Lesson 1 will cover generative AI for understanding data and model building. You will learn how data scientists can use generative AI to visualize, develop, and build models. Lesson 2 will focus on tools and techniques in exploratory data analysis (EDA) and developing predictive models using generative AI. You will also learn about industry-specific considerations, the challenges data scientists face, and the skills needed to succeedâalong with how generative AI can help sharpen those skills.
Final Project and Exam
- Enhance your data science with generative AI and complete the guided project and evaluation.