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  3. GenAI for Data Science Teams

GenAI for Data Science Teams

This course aims to demystify the complexities surrounding GenAI, making it accessible for data science professionals to leverage these technologies for enhancing data augmentation, automating repetitive tasks, and model development.

Reza Moradinezhad
Soheil Haddadi
Soheil HaddadiReza Moradinezhad
Data Science | core | 1 hour |   Published: Aug 2024
In partnership with:  Coursera

    Discussions

Overview

1KSTUDENTS*
98.3%RECOMMEND*

This course includes:

  • 1 hour of on-demand video  
  • Certificate of completion  
  • Direct access/chat with the instructor 
  • 100% self-paced online 
"GenAI for Data Science Teams" is an introductory course designed to bridge the gap between generative AI (GenAI) technologies and data science practices. This course aims to demystify the complexities surrounding GenAI, making it accessible for data science professionals to leverage these technologies for enhancing data augmentation, automating repetitive tasks, and model development. Through engaging content and practical applications, learners will gain a solid foundation in understanding how GenAI can revolutionize data handling, analysis, and model training processes.  Learners will explore the intersection of GenAI and data science, uncovering new opportunities for innovation and efficiency in their projects. The course covers the essential GenAI concepts tailored for data science applications, including data augmentation, synthetic data generation, automation of data preprocessing tasks, model development, and documentation. By the end of this course, learners will be equipped with the knowledge to creatively apply GenAI tools in their data science workflows, thereby enhancing their project outcomes and driving innovation within their teams. 

Skills You Will Gain

Data Preprocessing
Efficiency
Innovation
Project Outcomes
Synthetic Data Generation

Learning Outcomes (At the end of this program you will be able to)

  • Acquire a working knowledge of GenAI tools and strategies for using them to increase team collaboration and efficiency. 
  • Master the integration of GenAI technologies in enhancing data analytics and model development. 
  • Apply GenAI tools practically in real-world data science scenarios to improve outcomes. 
  • Navigate ethical, legal, and societal implications of using GenAI in data science. 
  • Analyze potential ethical concerns that are inherent in using GenAI for data science teams, including how to navigate challenges and ensure responsible implementation of AI technologies in data science practices. 
  • Create a working framework for staying ahead of GenAI advancements. 

Prerequisites

Learners should have a fundamental understanding of data science principles and strategies and an eagerness to learn and adapt to new technologies. 

Who Should Attend

This course is designed for data science team leaders and their teams, including Data Science Managers, Team Leads, and Senior Data Scientists aiming to enhance leadership and innovation through GenAI. It also targets aspiring data scientists and IT professionals, and software engineers seeking to understand GenAI applications for cross-disciplinary innovation.

 

 

Curriculum

Instructors

Frequently Asked Questions

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*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Module 1: GenAI for Data Science Teams
Reza Moradinezhad

Reza Moradinezhad

Reza is a passionate advocate for fostering effective and trustworthy collaboration between humans and artificial intelligence, with a strong commitment to advancing the ethical use of Generative AI. Holding a PhD in Computer Science from Drexel University, his research focuses on enhancing human trust in Embodied Virtual Agents (EVAs). Through collaborations with prestigious institutions such as MIT Media Lab, CMU HCII, Harvard University, and UCSD, Reza has contributed impactful research published in leading journals like Springer Nature, ACM CHI, and ACM C&C. His work has gained recognition from the academic community, earning him accolades such as the Outstanding Reviewer award by ACM ICMI 2019 and ACM CHI 2021. His research has also been featured in media outlets including The Next Web, TechXplore, and CBS News.

As an Assistant Teaching Professor at Drexel University's College of Computing and Informatics, Reza has shaped the minds of both undergraduate and graduate students, guiding them through complex topics such as Artificial Intelligence, Software Engineering, and Computer Graphics. His dedication to education extends beyond teaching, mentoring research projects on topics ranging from mind-wandering in the human brain to the effectiveness of creativity support tools in fostering innovation.

In addition to his academic work, Reza served as an AI Scientist at TulipAI, where he focused on ensuring the ethical and responsible application of Generative AI in media creation. He is driven by the vision of making AI more trustworthy for humanity and believes in designing transparent, fair, and responsible interactions with AI systems. Through his work, Reza aims to harness the full potential of AI while adhering to ethical principles and promoting responsible innovation.

With a proven track record in academic research, collaborative projects, and a deep passion for ethical AI development, Reza is committed to making significant contributions to the evolving field of Human-AI interaction.

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Soheil Haddadi

Soheil Haddadi

Soheil is a Postdoctoral Researcher with a strong academic foundation in Control and Automation Systems Engineering, specializing in Artificial Intelligence. His expertise spans across Data Science, Machine Learning, LLMs, Deep Learning, Natural Language Processing (NLP), and Robotics. Soheil has successfully applied his skills in both industry and research environments, contributing to advancements in AI.

Currently, as a Postdoctoral Researcher, Soheil is dedicated to pushing the boundaries of AI. His technical proficiency in Python, SQL, TensorFlow, and Keras, combined with a deep understanding of AI methodologies, has enabled him to deliver innovative solutions and insights. His prior work includes leading diverse projects such as sales forecasting using time series and LSTM neural networks, autonomous indoor navigation for quadrotors using ORB-SLAM, and deep learning-based credit scoring in the banking sector.

Soheil’s experience in data analysis and visualization further enhances his comprehensive understanding of how AI can address complex problems and create tangible value. His contributions are a testament to his dedication to advancing AI research and its real-world applications.

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Segment 01 - Introduction to GenAI for Data Scientists

Segment 02 - Resources for GenAI Usage for Data Science Teams

Segment 03 - The GenAI Future-Ready Framework for Data Science Teams

Segment 04 - Ethical Concerns and Remediating Risks

Segment 05 - Demo: Strategically Assign Project Subtasks with ChatGPT

Segment 06 - Collaborative Strategies with GenAI for Data Science Teams

Segment 07 - GenAI in Data Science Project Management

Segment 08 - GenAI for Boosting Creativity & Communication for Data Science Teams

Segment 09 - Best Practices for Protecting Against Plagiarism for Data Science Teams

Segment 10 - Practice Project for Data Science Team

Segment 11 - Closing Thoughts: What’s Next