In this course, you'll learn to contextualize qualitative and quantitative data to improve business decisions. You'll explore data collection tools, compare data-driven and data-inspired approaches, and understand why analysis can sometimes fail. You'll examine performance metrics and use data visualization to communicate the story behind the numbers. You'll study dashboard types, design principles, and mathematical thinking strategies to spot patterns to solve problems. Finally, you'll practice selecting the right analytical tools for different datasets based on their characteristics.
Make Data-Driven Decisions
Make Data-Driven Decisions
This course is part of Google Data-Driven Decision Making Specialization
Instructor: Google Career Certificates
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Discuss the importance and benefits of dashboards and reports to the data analyst with reference to Tableau and spreadsheets
Explain the difference between quantitative and qualitative data including reference to their use and specific examples
Compare and contrast data-driven decision making with data-inspired decision making
Discuss the use of data in the decision-making process
Details to know
Shareable certificate
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Recently updated!
September 2025
Taught in English
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This course is part of the Google Data-Driven Decision Making Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 3 modules in this course
Analysts contextualize individual data points and interpret them to inform business decisions. Qualitative and quantitative data are crucial elements of this process. You'll learn about data collection tools, how to compare data-driven and data-inspired decisions, and why data analysis can fail.
What's included
3 videos3 readings1 assignment
Data visualization and metrics are widely utilized to convert raw data into useful information. You'll learn tools for visualizing data, the types of dashboards, and how metrics are used to measure performance.
What's included
2 videos2 readings2 assignments
Mathematical thinking helps break down problems into smaller parts and identify the right tools for analysis, which often depend on dataset size. You'll also explore the characteristics, challenges, and benefits of big and small data.
What's included
1 video1 reading2 assignments
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Frequently asked questions
Data is a group of facts that can take many different forms, such as numbers, pictures, words, videos, observations, and more. We use and create data everyday, like when we stream a show or song or post on social media.
Data analytics is the collection, transformation, and organization of these facts to draw conclusions, make predictions, and drive informed decision-making.
No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.
You don't need to be a math all-star to succeed in this certificate. You need to be curious and open to learning with numbers (the language of data analysts). Being a strong data analyst is more than just math, it's about asking the right questions, finding the best sources to answer your questions effectively, and illustrating your findings clearly in visualizations.
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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.