Breaking into Data Science: Roles and Career Paths in the Age of GenAI

Overview

Explore how GenAI is reshaping Data Science careers, essential skills to develop, and practical steps to launch your journey in this field regardless of your background.

Syllabus

• Introduction to Data Science and GenAI
Overview of Data Science
Introduction to Generative AI (GenAI)
The impact of GenAI on Data Science careers
• Exploring Data Science Roles
Data Analyst
Data Engineer
Machine Learning Engineer
Data Scientist
AI Researcher
Career paths and transitions
• Essential Skills and Tools for Data Science in the Age of GenAI
Programming languages: Python, R
Data manipulation and analysis: Pandas, SQL
Machine learning frameworks: TensorFlow, PyTorch
Data visualization tools: Tableau, Matplotlib
Cloud platforms and tools: AWS, Google Cloud, Azure
Soft skills: Problem-solving, Communication, Collaboration
• GenAI-Specific Competencies
Understanding Generative Models
Working with GANs, VAEs, Transformers
Ethical considerations in AI
• Practical Steps to Launch Your Data Science Career
Building a strong foundation in statistics and mathematics
Learning and mastering key tools and technologies
Leveraging online courses and bootcamps
Building a portfolio with projects and case studies
Networking and community engagement
Preparing for data science interviews
• Adapting to the Evolving Landscape of Data Science
Continuous learning and upskilling strategies
Keeping abreast with the latest trends and research
Exploring interdisciplinary applications of data science
• Conclusion and Future Outlook
The future of GenAI in Data Science
Long-term career development opportunities

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Subjects

Data Science