Mastering Generative AI for Data Science from edX

With generative AI transforming the data science landscape, mastering its applications is crucial to data science professionals. This course equips you with cutting-edge generative AI skills tailored to the needs of a data scientist.

Through real-world scenarios, you will explore the use of generative AI throughout the data science lifecycle. Starting with data querying and preparation, you will learn how to leverage generative AI to generate, augment, and refine the data. You will also apply advanced AI techniques to develop and refine machine learning models while exploring ethical implications.

This course is designed for existing data scientists, data professionals, analysts, and engineers looking to thrive in the evolving AI landscape.

You’ll gain hands-on experience using generative AI tools, culminating in a course-end project that simulates a real-world scenario. Your project would make an excellent addition to prospective employers. Finally, you’ll also be completing a final quiz to obtain your certificate,

A basic understanding of generative AI, prompt engineering, and data science is essential to complete this course successfully.

What's inside

Learning objectives

Leverage various tools like gpt 3.5, chatcsv, tomat.ai, and so on, available for data scientists working with generative ai for querying and preparing data
Examine real-world scenarios where generative ai can enhance data science workflows

Practice generative ai skills in hands-on labs and projects by generating and augmenting datasets for specific use cases
Apply generative ai techniques in the development and refinement of machine learning models

Leverage various tools like gpt 3.5, chatcsv, tomat.ai, and so on, available for data scientists working with generative ai for querying and preparing data
Examine real-world scenarios where generative ai can enhance data science workflows
Practice generative ai skills in hands-on labs and projects by generating and augmenting datasets for specific use cases
Apply generative ai techniques in the development and refinement of machine learning models

Syllabus

Module 1: Data Science and Generative AI

Generative AI and Data Science

Generative AI’s Impact Across Industries

Types of Generative AI models

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Equips learners with generative AI skills, which are becoming increasingly crucial for data science professionals looking to stay competitive

Provides hands-on experience with generative AI tools, culminating in a project that simulates a real-world scenario for portfolio building

Explores the use of generative AI throughout the data science lifecycle, from data querying and preparation to model development and refinement

Requires a basic understanding of generative AI, prompt engineering, and data science, suggesting it is designed for those with some prior experience

Examines ethical considerations while using generative AI in industries, which is an important aspect of responsible AI development and deployment

Presented by IBM, a company recognized for its contributions to AI and machine learning, which may add credibility to the course content

Reviews summary

Applied generative ai for data science

According to learners, this course provides a practical approach to leveraging Generative AI throughout the data science lifecycle. Many highlight the value of the hands-on labs and project, finding them directly applicable to their work. The course covers the use of various relevant Generative AI tools for tasks like data preparation and model development. While generally well-received for its practical focus, some students note that certain sections can feel introductory if they already possess a strong background in Generative AI. It is important to meet the stated prerequisites to get the most out of the material. Overall, students view this as a valuable course for professionals seeking to integrate Gen AI into their data science workflows.

Requires existing background knowledge.

"The course moves at a good pace, but you definitely need the basic understanding of Gen AI and data science as stated."

"Having a foundation in prompt engineering was crucial for keeping up with some sections."

"I found it quite manageable because I had prior experience with the core data science concepts."

Covers use of specific Gen AI tools.

"Appreciated learning how to interact with specific tools like GPT-3.5 and ChatCSV for data tasks."

"The focus on practical tools that data scientists can use right away is a major plus."

"Saw demos and got to practice using tools relevant to the field."

Directly applicable to data science tasks.

"Excellent coverage of how Generative AI fits into the different stages of the data science workflow."

"This course showed me practical ways to use Gen AI for data preparation and analysis."

"Learned how to apply Gen AI techniques specifically for improving machine learning models."

Hands-on practice is highly valuable.

"The hands-on coding and projects are the strongest part of the course for me, providing real-world application."

"I particularly enjoyed the labs; they made applying the concepts much clearer and practical."

"The course-end project was great for consolidating learning and serves as a good portfolio piece."

Good overview, could go deeper in areas.

"Provides a solid foundation, but I wish it went into more depth on advanced model refinement techniques."

"Some initial modules felt a bit too much like a basic introduction to Gen AI for someone already working in DS."

"Could use more in-depth coverage on complex topics or optimization techniques."

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 Mastering Generative AI for Data Science with these activities:

Review Prompt Engineering Fundamentals

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Solidify your understanding of prompt engineering principles to effectively guide generative AI models for data science tasks.

Browse courses on Prompt Engineering

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Review key concepts of prompt engineering.
Practice writing effective prompts for different generative AI models.
Experiment with prompt variations to optimize model outputs.

Read 'Hands-On Generative AI with Python'

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Gain practical experience with generative AI models and techniques using Python.

View Hands-On Neural Networks with TensorFlow 2.0 on Amazon

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Read the chapters related to your areas of interest.
Implement the code examples provided in the book.
Experiment with different model architectures and datasets.

Read 'Generative AI with Python and TensorFlow 2'

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Explore advanced generative AI techniques and implementations using Python and TensorFlow 2.

View Generative AI with Python and TensorFlow 2:... on Amazon

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Read the chapters related to GANs and VAEs.
Implement the code examples provided in the book.
Experiment with different model architectures and hyperparameters.

Four other activities

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Experiment with different prompt variations

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Improve your prompt engineering skills by experimenting with different prompt variations and analyzing the outputs.

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Choose a generative AI model and a specific task.
Create a set of prompts with different variations.
Run the prompts and analyze the outputs.
Identify the most effective prompt variations.

Develop a Generative AI-Powered Data Augmentation Tool

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Apply generative AI techniques to create a tool that automatically augments datasets for improved model training.

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Select a dataset and a generative AI model for data augmentation.
Implement the data augmentation pipeline using the chosen model.
Evaluate the impact of data augmentation on model performance.
Refine the tool based on evaluation results.

Create a Blog Post on Ethical Considerations in Generative AI for Data Science

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Deepen your understanding of ethical implications by researching and writing about responsible use of generative AI in data science.

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Research ethical considerations related to generative AI.
Outline the key points for your blog post.
Write the blog post, providing examples and insights.
Edit and proofread the blog post.

Build a Data Visualization Dashboard using Generative AI

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Develop a dashboard that leverages generative AI to create insightful data visualizations.

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Select a dataset and define the visualization goals.
Use generative AI to generate different visualization options.
Choose the most effective visualizations and integrate them into the dashboard.
Add interactive elements and refine the dashboard design.

Career center

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

Data Scientist

The role of a Data Scientist is to analyze complex data sets to extract meaningful insights and develop data-driven solutions. Data scientists use statistical modeling, machine learning techniques, and data visualization tools to identify trends, patterns, and anomalies in data. This course, "Mastering Generative AI for Data Science," directly addresses the evolving needs of a data scientist by providing hands-on experience with generative AI tools applicable to the entire data science lifecycle. It covers data querying, preparation, augmentation, and refinement using generative AI. A data scientist can enhance their ability to develop and refine machine learning models using advanced AI techniques discussed in the course. The final project can be used to show mastery of the topic to prospective employers.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer focuses on building, deploying, and maintaining machine learning models and systems. They work closely with data scientists to translate models into production-ready code and ensure scalability and reliability. This course helps a machine learning engineer by exploring how Generative AI can be used in tandem with data science. Specifically, the course covers using AI to augment datasets for specific use cases. This will help a machine learning engineer with model training and refinement. The course also emphasizes the ethical implications of using Generative AI, which is essential for responsible model development.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers are responsible for designing, building, and maintaining the infrastructure that supports data storage, processing, and analysis. They work with large datasets and use various technologies to ensure data quality and accessibility. This course can help a data engineer by exploring how Generative AI can improve their operations. The course covers data querying and preparation. A data engineer can then learn how to leverage generative AI to generate, augment, and refine data. This is directly applicable to building better data pipelines and ensuring data quality.

See salaries and explore the career path for Data Engineer

AI Ethicist

An AI Ethicist focuses on the ethical implications of artificial intelligence, ensuring that AI systems are developed and used responsibly and in alignment with human values. This course can help an AI Ethicist by covering considerations and challenges while using generative AI in industries. Furthermore, it examines real-world scenarios where generative AI can enhance data science workflows. An AI Ethicist will be able to use the information from this course to advise organizations on the responsible and ethical use of generative AI.

See salaries and explore the career path for AI Ethicist

Data Analyst

Data Analysts are responsible for collecting, processing, and analyzing data to identify trends and insights that can inform business decisions. They create reports and visualizations to communicate their findings to stakeholders. This course can help a data analyst increase the value of their work. The course covers using Generative AI for data insights and data visualization. This can empower a data analyst to create more compelling and data-driven narratives. Furthermore, the analyst will learn Generative AI tools for querying and preparing data, which can streamline the data analysis process.

See salaries and explore the career path for Data Analyst

Data Architect

Data Architects design and implement data management systems, including databases and data warehouses. They ensure data is stored efficiently and securely, and that it is easily accessible to end-users. This course can help a data architect implement new technologies to help make data even more accessible. The course covers generative AI for data preparation and querying. It also covers the considerations for using generative AI in various industries. This course, in particular, will help a data architect integrate Generative AI into their work.

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Analytics Manager

An Analytics Manager leads a team of analysts to provide data-driven insights and recommendations to business stakeholders. This individual sets the strategic direction for the analytics function and ensures the team delivers high-quality analysis. This course can help an analytics manager lead their team by exposing them to the latest trends in AI. The course covers real-world scenarios where generative AI can enhance data science workflows. This is relevant for making decisions about how to direct a team of data scientists, and how to better equip them for success.

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AI Product Manager

An AI Product Manager is responsible for defining the vision, strategy, and roadmap for AI-powered products. They collaborate with engineering, design, and marketing teams to bring AI products to market. This course can help an AI product manager understand the challenges and opportunities of deploying AI. The course covers considerations while using generative AI, and its impact on various industries. Furthermore, the course discusses using generative AI for data insights. This is useful for understanding a customer's needs and incorporating them into the product.

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Statistician

Statisticians analyze data, develop statistical models, and interpret results to support decision-making and solve problems in various fields. This often requires an advanced degree, such as a master's. This course can help a statistician because it directly applies to their field. The course covers using generative AI to develop and refine models. These models can then be used in statistical analysis. The course also discusses the challenges while using generative AI in various industries. This is directly applicable to the decisions a statistician faces.

See salaries and explore the career path for Statistician

Business Intelligence Analyst

A Business Intelligence Analyst focuses on analyzing data to identify business trends, create reports, and develop dashboards to support decision-making. This role requires skills in data analysis, data visualization, and business acumen. This course may be useful for a business intelligence analyst to learn about generative AI’s impact across various industries and for data visualization. The course also discusses using generative AI for data querying. This course explores using generative AI for data insights, which will improve a Business Intelligence Analyst's ability to find patterns and trends in data.

See salaries and explore the career path for Business Intelligence Analyst

AI Consultant

An AI Consultant advises organizations on how to implement AI solutions to improve their operations and achieve their strategic goals. They assess business needs, recommend appropriate AI technologies, and guide the implementation process. This course, "Mastering Generative AI for Data Science," may be useful to an AI consultant by providing them with knowledge of cutting-edge generative AI skills tailored to the needs of businesses. The course covers real-world scenarios where generative AI can enhance data science workflows, providing valuable insights for consulting engagements. Understanding how generative AI can be used for data generation, augmentation, and preparation, as covered in the course, is also relevant.

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AI Researcher

An AI Researcher investigates new AI techniques and algorithms, contributing to the advancement of the field. This often requires understanding of advanced mathematics and computer science, usually requiring a master's degree or doctorate. This course may be useful to an AI researcher because it covers the types of generative AI models. Furthermore, the course covers using Generative AI to understand data and model development. Having an understanding of generative AI's impact across industries is also useful to ensure applicability of the AI research.

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Research Scientist

A Research Scientist conducts scientific research and experiments in a specific field. This often requires a doctoral degree. This course may be useful to a research scientist because it covers generative AI's impact across various industries. This can help a research scientist identify new areas for research. The course also covers best practices for using generative AI, and the challenges of using generative AI. Overall, this course can help equip a research scientist with a new set of skills.

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Bioinformatician

Bioinformaticians analyze biological data using computational tools and statistical methods. They work in research institutions, pharmaceutical companies, and healthcare organizations to extract insights from genomic, proteomic, and other biological datasets. This often requires an advanced degree. This course, "Mastering Generative AI for Data Science," may be useful to a bioinformatician by introducing them to generative AI skills. This course also covers augmenting datasets for specific use cases. Biological datasets are frequently large and unwieldy, and can be augmented for new insights using skills from this course.

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Quantitative Analyst

Quantitative Analysts, often called Quants, use mathematical and statistical models to analyze financial markets, manage risk, and develop trading strategies. This often requires an advanced degree in a STEM field. This course may be useful to a quantitative analyst because it touches on the kinds of skills a Quant might want to develop. The course covers generative AI for understanding data and model development. This might be applicable to the financial markets, but more information would be needed to determine this.

See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mastering Generative AI for Data Science.

Hands-On Neural Networks with TensorFlow 2.0

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Offers a practical introduction to generative AI using Python. It covers various generative models and techniques, including GANs, VAEs, and transformers. It is particularly useful for data scientists who want to gain hands-on experience with generative AI. This book provides a solid foundation for understanding and implementing generative AI models.

Hands-On Neural Networks with TensorFlow 2.0

Paperback

Hands-On Neural Networks with TensorFlow 2.0:...

Kindle Edition

Generative AI with Python and TensorFlow 2

Save

Provides a practical guide to building generative AI models using Python and TensorFlow 2. It covers various generative techniques, including GANs, VAEs, and transformers. It valuable resource for data scientists looking to implement generative AI solutions. This book adds depth to the course by providing hands-on examples and code snippets.

Generative AI with Python and TensorFlow 2: Create...

Paperback

Mastering Generative AI for Data Science

What's inside

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Reviews summary

Applied generative ai for data science

Activities

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Reading list

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