We may earn an affiliate commission when you visit our partners.

GenAI in Data Analytics

In this short course, you learn what generative AI is and how it functions. You also discover how GenAI can be applied in different business scenarios as well as navigating the concerns around its usage. Then you explore how to incorporate GenAI into your data analytics efforts to streamline processes and improve data quality.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until December 2

Annual Subscription

Invest in your career growth and save. Start your annual subscription to Coursera Plus and access 10,000+ programs.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops professional skills or deep expertise in GenAI and data analytics pipelines

This course will help streamline processes by incorporating GenAI, guided by instructors with solid industry experience, Anke Audenaert

Offers hands-on labs and interactive materials for a practical understanding of GenAI applications

Covers topics such as usage concerns, business applications, and data quality improvement, offering a comprehensive understanding of GenAI's role in data analytics

Multi-modal course format includes a blend of videos, readings, and discussions for a well-rounded learning experience

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Genai fundamentals for data professionals

According to learners, this course offers a positive and well-structured introduction to GenAI in data analytics, effectively bridging the gap between AI concepts and their practical application. Many found the explanations of complex concepts to be clear and concise, appreciating the emphasis on real-world business scenarios and ethical considerations. While it provides a solid theoretical foundation, some students noted a desire for more hands-on coding exercises or project-based learning opportunities, suggesting it might be best suited for beginners or those new to GenAI in a data context, rather than advanced practitioners seeking deep technical insights.

Ideal for beginners; may be too basic for advanced learners.

"The course provides a good overview of GenAI, but for someone with prior AI knowledge, it felt a bit too basic."

"It's a good starting point for beginners, but don't expect to become an expert."

"It's an okay course. It covers the basics of GenAI and its relevance to data analytics."

Complex GenAI topics are explained clearly and engagingly.

"The explanations of complex concepts were clear and concise."

"The way it broke down complex GenAI concepts into understandable segments was fantastic."

"The concepts were explained clearly, and the presentation was engaging."

Focuses on real-world applications, business scenarios, ethics.

"I particularly appreciated the focus on practical business scenarios and the discussion around ethical considerations."

"The emphasis on real-world applications and ethical considerations was a huge plus."

"The ethical concerns section was particularly relevant."

Provides a comprehensive and clear introduction to GenAI.

"This course provided an excellent introduction to GenAI's application in data analytics."

"A solid foundation! The course is great for understanding what GenAI is and how it functions within a data context."

"Excellent course! It provided a comprehensive understanding of GenAI and its impact on data analytics."

Lacks extensive practical coding or project-based exercises.

"My only minor critique is that I would have liked more hands-on coding exercises or deeper dives into specific library implementations."

"I think the course could benefit from some more practical, project-based learning opportunities."

"There were too few practical examples for integration, and the content often seemed to scratch the surface."

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 GenAI in Data Analytics with these activities:

Organize and Review Course Materials

Show steps

Stay organized and maximize your learning by regularly reviewing and compiling your course materials. This will help you retain the information more effectively.

Show steps

Create a dedicated space for storing and organizing your notes, assignments, and readings
Review your notes and summaries regularly to reinforce your understanding

Show all one activities

Career center

Learners who complete GenAI in Data Analytics will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 48 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 GenAI in Data Analytics.

Fundamentals of Business Process Management

Save

Provides a comprehensive overview of Business Process Management (BPM), covering its concepts, methods, and tools. It is suitable for gaining a broad understanding of how to analyze, design, implement, and monitor business processes. Often used as a textbook in university programs, it's a valuable reference for both students and professionals.

Fundamentals of Business Process Management

Hardcover

Fundamentals of Business Process Management

Kindle Edition

Data Science for Business

Save

Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.

GenAI in Data Analytics

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Genai fundamentals for data professionals

Activities

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner