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Generative AI for Data Science

Microsoft

Did you know Generative AI can enhance data accuracy and operational efficiency in data science?

This Short Course was created to help data scientists and AI enthusiasts unlock the full potential of Generative AI in their data-driven projects.

Within this 3-hour-long commitment, you will learn how to explore and leverage GenAI applications, identify key use cases like data augmentation and anomaly detection, and analyze crucial data security and privacy issues.

Read more

Did you know Generative AI can enhance data accuracy and operational efficiency in data science?

This Short Course was created to help data scientists and AI enthusiasts unlock the full potential of Generative AI in their data-driven projects.

Within this 3-hour-long commitment, you will learn how to explore and leverage GenAI applications, identify key use cases like data augmentation and anomaly detection, and analyze crucial data security and privacy issues.

By completing this course, you'll be able to apply advanced AI techniques to real-world data challenges, ensuring your projects are both innovative and ethically sound.

Blending cutting-edge AI technology with practical, industry-specific applications makes this course unique. To be successful in this project, you will need a solid foundation in Python, basic machine learning principles and an understanding of fundamental data science concepts.

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What's inside

Syllabus

Generative AI for Data Science
Upon completing this course, you will be proficient in harnessing the transformative capabilities of generative AI (GenAI) within the data science landscape, specifically in marketing and advertising. Additionally, you will explore the ethical and operational implications of GenAI in data science. By the end of the course, you will be equipped to integrate the innovative potentials of GenAI technologies into your practices, effectively balancing innovation with integrity.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data scientists and AI enthusiasts seeking to enhance their skills in generative AI and its applications
Provides practical, industry-specific applications of generative AI in data science
Leverages the expertise of Microsoft, a leading technology company in the field of AI
Requires a solid foundation in Python, machine learning principles, and data science concepts
Covers ethical considerations and operational implications of generative AI in data science
May require additional resources for learners who are new to the field of generative AI

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Reviews summary

Practical generative ai for data scientists

According to students, this course offers a positive and concise introduction to Generative AI for data science. Learners particularly appreciate the practical applications and hands-on demonstrations, especially those focusing on local LLM deployment and feature engineering with SMS campaign data. The course is noted for its coverage of ethical considerations in AI. While largely positive, some students highlight that it moves at a fast pace and truly requires a solid foundation in Python and machine learning concepts, making it best suited for those with prior experience.
Course assumes a strong background in data science fundamentals.
"It truly requires a pre-existing understanding of data science fundamentals, so it's not for absolute beginners."
"You definitely need to be comfortable with Python and ML concepts beforehand, as it moves very quickly."
"I struggled a bit without having a very strong Python background; it felt like it assumed too much prior knowledge."
Addresses crucial ethical and privacy aspects of AI.
"The ethical considerations module was very insightful and highlighted important responsibilities in AI."
"I specifically found the discussions on data security and privacy in GenAI to be very relevant and well-covered."
"The course helped me analyze the security and privacy impacts of Generative AI, emphasizing responsible development."
Course materials are generally well-presented and easy to follow.
"The course materials were clear and concise. It’s a great way to quickly grasp key concepts."
"I appreciated the step-by-step code walkthroughs, which made complex topics understandable."
"My understanding of GenAI's impact on data science was greatly solidified due to the clear explanations."
Focuses on real-world scenarios and use cases.
"The segment on local LLM deployment using LoRA and quantization was incredibly practical and directly applicable to my work."
"A solid introduction to GenAI's role in data science. The feature engineering with SMS data tutorial was unique and well-explained."
"I found the practical exercises extremely beneficial for understanding real-world use cases, especially in data augmentation and anomaly detection."
The course content is delivered at a high speed.
"My only minor critique is that it moves very quickly, so prior knowledge is essential."
"I found this course somewhat disappointing; the pace felt rushed and the explanations were sometimes superficial."
"Good if you want a general idea, but not for in-depth mastery, as it covers a lot in a short time."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI for Data Science with these activities:
Review basic Python syntax and concepts
Refresh your understanding of Python syntax and programming concepts to enhance your starting point in the course.
Browse courses on Python
Show steps
  • Review tutorials or documentation on basic Python syntax.
  • Complete practice exercises on data types, variables, and control flow.
  • Work through examples of simple Python programs to reinforce your understanding.
Organize and Review Course Materials for Future Reference
Organizing and reviewing your course materials will help you retain information better and make it easier to find what you need when you need it
Show steps
  • Create a system for organizing your notes, assignments, and other materials
  • Regularly review your materials to reinforce your learning
Explore online tutorials on building and deploying generative AI models
Supplement your learning by following guided tutorials to build and deploy generative AI models.
Browse courses on Generative AI
Show steps
  • Identify online tutorials from reputable sources covering generative AI model development.
  • Follow the tutorials step-by-step, implementing the techniques and algorithms.
  • Experiment with different parameters and settings to observe the impact on model performance.
13 other activities
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Read 'Interpretable Machine Learning' by Christoph Molnar
Reading this book will provide you with a solid foundation in interpretable machine learning techniques, including SHAP, LIME, and ICE, which are essential for understanding and explaining the predictions of AI models.
View Interpretable Machine Learning on Amazon
Show steps
  • Read through the key concepts of interpretable machine learning
  • Complete the end-of-chapter exercises to test your understanding
  • Apply the techniques you learn to your own data science projects
Follow along with a tutorial on data cleansing and preprocessing for generative AI models
Improve your understanding of the crucial steps involved in preparing data for generative AI, ensuring your models are trained on high-quality and relevant data.
Browse courses on Data Preprocessing
Show steps
  • Identify a comprehensive tutorial that covers data cleansing and preprocessing techniques for generative AI
  • Set aside dedicated time to follow the tutorial step-by-step, practicing the techniques on a suitable dataset
  • Experiment with different data cleaning and preprocessing methods to optimize your model's performance
Complete coding exercises on generative AI techniques
Strengthen your programming skills and understanding of generative AI algorithms through practice.
Browse courses on Generative AI
Show steps
  • Find coding exercises or challenges related to generative AI techniques.
  • Implement the algorithms in Python or another programming language.
  • Test and debug your code to ensure it produces the expected results.
Create a simple data augmentation pipeline
Develop a practical understanding of data augmentation techniques by building your own pipeline.
Browse courses on Data Augmentation
Show steps
  • Choose a dataset and identify augmentation techniques suitable for it.
  • Implement the data augmentation pipeline using Python libraries.
  • Evaluate the effectiveness of your pipeline on model performance.
Step-by-Step Guide to Building a Generative AI Model in Python
Following this guide will give you hands-on experience in building your own generative AI model using Python, providing you with a deeper understanding of the process and the challenges involved.
Browse courses on Generative AI
Show steps
  • Set up your Python environment and install the necessary libraries
  • Load and preprocess your data
  • Train your generative AI model
  • Evaluate your model's performance
Complete Generative AI Coding Challenges on LeetCode
Practicing coding challenges on LeetCode will help you improve your programming skills and reinforce your understanding of Generative AI concepts
Browse courses on Generative AI
Show steps
  • Sign up for a LeetCode account
  • Filter for Generative AI-related problems
  • Attempt to solve the problems on your own
  • Review the solutions and learn from your mistakes
Practice using Generative AI to enhance data augmentation for machine learning
Solidify your understanding of how Generative AI can be leveraged to generate synthetic data, expanding your training datasets and improving model performance.
Browse courses on Data Augmentation
Show steps
  • Choose a suitable dataset and identify areas where data augmentation can be beneficial
  • Select a generative AI model and experiment with different hyperparameters to generate synthetic data
  • Incorporate the synthetic data into your machine learning pipeline and evaluate the impact on model performance
Engage in peer discussions on the ethical implications of Generative AI
Foster critical thinking and ethical awareness by actively participating in discussions with peers, exploring the potential risks and benefits of Generative AI and its implications on society.
Show steps
  • Identify a relevant peer group or discussion forum focused on Generative AI ethics
  • Actively engage in discussions, sharing your perspectives and listening attentively to diverse viewpoints
  • Conduct additional research on specific ethical concerns related to Generative AI to inform your contributions
Present an overview of Generative AI use cases
Deepen your understanding of Generative AI applications by creating a presentation that highlights its practical uses.
Browse courses on Generative AI
Show steps
  • Research and gather information about various Generative AI use cases across industries.
  • Structure your presentation to clearly explain the benefits and limitations of each use case.
  • Create visual aids such as charts, graphs, or examples to illustrate your points.
Develop a content piece showcasing the applications of Generative AI in data science
Enhance your comprehension by creating a comprehensive content piece, such as a blog post or presentation, that explores the practical applications of Generative AI within the field of data science.
Browse courses on Generative AI Applications
Show steps
  • Conduct thorough research on the applications of Generative AI in data science, identifying specific use cases and examples
  • Determine the target audience and choose a suitable format for your content, such as a blog post, presentation, or infographic
  • Develop engaging and informative content that showcases the benefits and potential of Generative AI in data science
  • Share your content with the intended audience and engage in discussions to enhance your understanding
Develop a Case Study on Using Generative AI for Data Augmentation
Creating a case study will allow you to apply your knowledge of generative AI to a real-world problem, demonstrating your understanding of the technology's capabilities and limitations.
Browse courses on Generative AI
Show steps
  • Identify a business problem that can be solved using data augmentation
  • Collect and prepare a dataset for your project
  • Train a generative AI model to augment your data
  • Evaluate the performance of your augmented data
  • Write up your case study, including your findings and recommendations
Build a Generative AI Application for Text Summarization
Building a project will allow you to apply your knowledge of generative AI to a practical problem, demonstrating your ability to use the technology to solve real-world challenges
Browse courses on Generative AI
Show steps
  • Define the scope of your project
  • Gather and prepare your data
  • Train a generative AI model for text summarization
  • Build a user interface for your application
  • Deploy your application and test its performance
Contribute to an open-source generative AI project
Gain practical experience and contribute to the generative AI community by participating in open-source projects.
Browse courses on Generative AI
Show steps
  • Identify open-source generative AI projects aligned with your interests and skills.
  • Review the project documentation and identify areas where you can contribute.
  • Submit bug reports, feature requests, or code contributions to the project.

Career center

Learners who complete Generative AI for Data Science will develop knowledge and skills that may be useful to these careers:

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180 minutes
Level
Intermediate
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Coursera
Institution
Microsoft
Instructor
Microsoft
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English
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Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data scientists and AI enthusiasts seeking to enhance their skills in generative AI and its applications
Provides practical, industry-specific applications of generative AI in data science
Leverages the expertise of Microsoft, a leading technology company in the field of AI
Requires a solid foundation in Python, machine learning principles, and data science concepts
Covers ethical considerations and operational implications of generative AI in data science
May require additional resources for learners who are new to the field of generative AI
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