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Learner Reviews & Feedback for Foundations of Data Science by Google

4.7
stars
3,764 ratings

About the Course

This is the first of eight courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills...

Top reviews

IA

Oct 16, 2025

the labs were sufficients and well organised. The follow along labs prepare you for the pratice labs and allow you to grab more of the instructional videos when you review them. Thanks coursera

LS

Oct 15, 2025

I believe a focus on the planning phase should come after getting familiar with the technical parts, as I feel like that is context you are missing to fully understand how to plan a data project.

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By Abdul K

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Jan 5, 2026

Good

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Jan 1, 2026

nice

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Dec 22, 2025

cool

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Dec 7, 2025

done

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Apr 16, 2025

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Feb 16, 2025

nice

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GOOD

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Jan 26, 2025

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Jan 7, 2025

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Dec 31, 2024

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Dec 5, 2024

cool

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Oct 29, 2024

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Oct 8, 2024

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Sep 29, 2024

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Sep 29, 2024

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Sep 1, 2024

Good