Google Data-Driven Decision Making | Coursera

在这个"双十一"，致力于职业发展 。Coursera Plus全年会员享8折优惠。
Improve business decisions with data and AI.
Learn to ask SMART questions, analyze information, and turn complex data into actionable insights.
位教师：Google Career Certificates
包含在 Coursera Plus•了解更多 中
Apply structured thinking, data integrity checks, and ethical practices to real-world business problems
Use AI responsibly to speed up data analysis and prepare effective presentations
Communicate insights effectively to stakeholders and make confident, data-driven decisions
Ready to harness the power of data and AI to make better business decisions? This beginner-friendly Specialization from Google will equip you with the essential data analysis skills you need.
You'll learn how to ask the right questions, analyze information, and turn complex data into clear, actionable insights. The program begins by exploring core concepts of data analytics and structured thinking, then progresses to topics like data integrity, bias, and ethical responsibility.
Throughout the program, Google experts will guide you through this Specialization by providing hands-on practice with popular tools like spreadsheets, and generative AI tools, learning how to:
Ask the right questions to get to the heart of a problem
Discover hidden insights
Master the art of presenting your findings and influencing key stakeholders
By the time you're finished, you won't just know how to analyze data—you'll be able to manage real-world business scenarios with confidence.
Throughout this Specialization, you’ll engage in hands-on activities that simulate real-world data scenarios. You’ll clean and validate datasets, detect bias, and apply structured problem-solving to business problems. You’ll use spreadsheets to organize and calculate data and explore the art of visualizations and how to create them. You’ll learn how to use AI prompting frameworks to accelerate analysis and build presentations.
Discuss the use of data in everyday life decisions
Explain the data analysis process, making specific reference to the ask, prepare, process, analyze, share, and act phases
Define key concepts involved in data analytics including data, data analysis, and data ecosystem
Explain the concept of data-driven decision-making including specific examples
Explore problem solving in data analysis through a variety of real-world business scenarios
Describe the six phases of the data analysis process
Identify the common problem types that data analysts face
Practice effective questioning techniques that can help guide analysis
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
Identify common limitations with data, with specific reference to speed versus accuracy and responding to time-sensitive requests
Discuss the importance of focus on stakeholder expectations.
Discuss best practices for the data analyst in reference to office communication, conflict resolution, facilitating meetings, and status reports.
Discuss the difference between biased and unbiased data.
Discuss characteristics of credible sources of data including reference to untidy data
Explain the relationship between data ethics and data privacy
Demonstrate an awareness of the accessibility issues associated with open data
Define different types of data integrity and identify risks to data integrity.
Describe the benefits of documenting the data cleaning process.
Describe strategies that can be used to address insufficient data.
Describe the benefits of documenting the data cleaning process
Describe the use of data visualizations to talk about data and the results of data analysis.