Google Data-Driven Decision Making

Successful data analysts balance the needs and expectations of their team and the stakeholders they support. In this course, you’ll learn strategies for managing stakeholder expectations while establishing clear communication with your team. By the end of this course, you will be able to: - Understand communication best practices for the data analyst, including reference to office communication, conflict resolution, facilitating meetings, and status reports - Discuss the importance of focus on stakeholder expectations - Identify common limitations with data, with specific reference to speed versus accuracy and responding to time-sensitive requests

In this course, you'll learn about problem solving in data analysis through real-world scenarios. You’ll explore the phases of data analysis and the problem types that data analysts face. You’ll also learn how to ask effective questions to guide data analysis. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analytics tasks. By the end of this course, you will be able to: - 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

Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy. By the end of this course, you will be able to: - Explain what is involved in reviewing data to identify bias - Discuss the difference between biased and unbiased data - Identify different types of bias including confirmation, interpretation, and observer bias - Discuss characteristics of credible sources of data including reference to untidy data - Explain the concept of open data with reference to the ongoing debate in data analytics - Define data ethics and data privacy - Explain the relationship between data ethics and data privacy - Demonstrate an understanding of the benefits of anonymizing data - Demonstrate an awareness of the accessibility issues associated with open data

In this course, you’ll discover how to give an effective presentation about your data analysis. The course will teach you how to construct insightful presentations that resonate with your audience. You'll learn to anticipate and address potential questions and to articulate the limitations of your data, ensuring a robust and credible narrative for your stakeholders. By the end of this course, you will be able to: - Describe best practices for addressing the question-and-answer section of a presentation - Consider the caveats and limitations associated with the data in a presentation - Differentiate between strong and weak presentation content - Describe how junior data analysts are expected to use their presentation skills - Explain principles and practices associated with effective presentations - Identify appropriate responses to presentation objections

Need to crunch data or pull together a presentation? In this course, you’ll design prompts that help you extract insights, understand spreadsheet formulas, and build graphs to visualize data. You'll also explore how to use AI tools to help with speaker notes and practice before your big day, once you’re ready to present your findings. By the end of this course, you will be able to: - Develop a responsible prompting practice when entering data into generative AI tools - Apply the prompting framework to extract insights from data, identify and fix spreadsheet formulas, and explore data visualization - Understand AI sampling parameters, how they work, and strategies for their use - Apply the prompting framework to level up your presentation skills

Data is everywhere, but turning it into insight requires more than technical skills. You'll learn about bias and its appearance in data, sharpen your analytical thinking skills, and understand how data ecosystems operate. You’ll learn to question assumptions, interpret findings by combining data and business knowledge, and understand why data-informed decisions empower businesses. By the end of this course, you will be able to: -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 -Describe the key characteristics of analytical thinking -Explain how analytical thinking enables decision-making -Begin asking more effective questions

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. By the end of this course, you will be able to: • 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 • Differentiate between data and metrics, giving specific examples • Demonstrate an understanding of what is involved in using a mathematical approach to analyze a problem

Data integrity is critical to successful analysis. In this course, you’ll explore methods and steps that analysts take to check their data for integrity. This includes knowing what to do when you don’t have enough data. You’ll also learn about sample size and understand how to avoid sampling bias. All of these methods will help you ensure your analysis is successful. By the end of this course, learners will: - Define data integrity with reference to types and risks. - Check for data integrity. - Identify common pitfalls when cleaning data. - Describe the benefits of documenting the data cleaning process. - Describe strategies that can be used to address insufficient data. - Verify the results of cleaning data.