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  1. Courses
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  3. GenAI for Data Scientists

GenAI for Data Scientists

This course will cover the basics of machine learning models, data augmentation, and ethical considerations, offering insights into how GenAI can enhance analytical precision and innovation in data science.

Reza Moradinezhad
Soheil Haddadi
Soheil HaddadiReza Moradinezhad
Data Science | core | 1 hour 10 minutes |   Published: Jul 2024
In partnership with:  Coursera

    Discussions

Overview

1KSTUDENTS*
96%RECOMMEND*

This course includes:

  • 70 minutes of on-demand video  
  • Certificate of completion  
  • Direct access/chat with the instructor 
  • 100% self-paced online 
"GenAI for Data Scientist" is tailored for professionals eager to incorporate Generative AI (GenAI) into their data science practice. This introductory course demystifies GenAI, highlighting its transformative impact on data analysis, predictive modeling, and beyond. Learners will learn not only the technical workings of GenAI tools but also their practical applications in real-world data science scenarios. The course will cover the basics of machine learning models, data augmentation, and ethical considerations, offering insights into how GenAI can enhance analytical precision and innovation in data science.  The course aims to build a strong foundational understanding of GenAI, enabling data scientists to explore innovative ways to apply these technologies in their daily tasks. By the end of the course, Learners will have a clear grasp of the capabilities and limitations of GenAI, setting the stage for more advanced studies or immediate application in their professional roles. 

Skills You Will Gain

Data Science
Ethical AI
Generative AI
Machine Learning
Predictive Modeling

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

  • Identify the capabilities of GenAI for data scientist functions. 
  • Examine real-world applications to leverage GenAI for streamlining work and fostering innovation in data scientist functions. 
  • Compare and contrast the risks, challenges, and ethical considerations of GenAI in the workplace for Data Scientist functions in general.  

Prerequisites

  • Familiarity with fundamental data science concepts and workflows
  • Curiosity and willingness to explore new technologies

Who Should Attend

This course is designed for team leads and managers looking to incorporate GenAI into their strategic initiatives, as well as individual data scientists and analysts eager to enhance their daily tasks with advanced GenAI techniques. It is also suitable for professionals aiming to advance their careers by mastering cutting-edge GenAI applications in data science.

Curriculum

Instructors

Frequently Asked Questions

How much do the courses at Starweaver cost?

We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.

Do you offer any certifications upon completion of a course at Starweaver?

Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

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No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.

Are Starweaver's courses designed for beginners or advanced students?

Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

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Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*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 Scientists
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 - GenAI and Data Science: Glossary

Segment 03 - History & Background for GenAI and Data Science

Segment 04 - Demo: Generating Synthetic Data and Machine Learning Model Code with Gemini

Segment 05 - Demo: Text Classification with the OpenAI API

Segment 06 - Demo: Converting Python Code to other Programming Languages with CodeConvertAI

Segment 07 - Remediating Risks and Ethical Concerns

Segment 08 - Demo: Synthetic Data Creation and Predictive Model Building with Gemini

Segment 09 - Demo: Exploratory Data Analysis (EDA) with ChatGPT

Segment 10 - Practice Project: Synthetic Data Showcase

Segment 11 - Closing Thoughts: What’s Next