Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.
- Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job
- Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau)
- Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming
- Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms
- ACE® College Credit Recommendation: Up to 12 credits toward select universities in the US
- Google Career Certificates Employer Consortium: Access to 150+ top employers (Google, Accenture, Deloitte, Verizon, and more)
- 2.8M+ learners and 75% of U.S. grads report a positive career outcome within 6 months
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Foundations: Data, Data, Everywhere
- Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems.
- Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking.
- Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics.
- Describe the role of a data analyst with specific reference to jobs.
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Ask Questions to Make Data-Driven Decisions
- Explain how the problem-solving road map applies to typical analysis scenarios.
- Discuss the use of data in the decision-making process.
- Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.
- Describe the key ideas associated with structured thinking.
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Prepare Data for Exploration
- Explain what factors to consider when making decisions about data collection.
- Discuss the difference between biased and unbiased data.
- Describe databases with references to their functions and components.
- Describe best practices for organizing data.
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Process Data from Dirty to Clean
- Define different types of data integrity and identify risks to data integrity.
- Apply basic SQL functions to clean string variables in a database.
- Develop basic SQL queries for use on databases.
- Describe the process of verifying data cleaning results.
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Analyze Data to Answer Questions
- Discuss the importance of organizing your data before analysis by using sorts and filters.
- Convert and format data.
- Apply the use of functions and syntax to create SQL queries to combine data from multiple database tables.
- Describe the use of functions to conduct basic calculations on data in spreadsheets.
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Share Data Through the Art of Visualization
- Describe the use of data visualizations to talk about data and the results of data analysis.
- Identify Tableau as a data visualization tool and understand its uses.
- Explain what data driven stories are including reference to their importance and their attributes.
- Explain principles and practices associated with effective presentations.
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Data Analysis with R Programming
- Describe the R programming language and its programming environment.
- Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.
- Describe the options for generating visualizations in R.
- Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.
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Capstone Project
- Identify the key features and attributes of a completed case study.
- Apply the practices and procedures associated with the data analysis process to a given set of data.
- Discuss the use of case studies/portfolios when communicating with recruiters and potential employers.
- Gain a competitive edge by learning AI skills from Google experts.
This repo is open source! Feel free to:
- 👀 Browse the course readings, exercises, and case studies
- 💻 Fork/clone for your own self-study or review
- 🤝 Collaborate by submitting issues or improvements via pull requests
- 🌟 Get inspired if you’re preparing to be a data professional or want to level up your data skills
Disclaimer: All content is for educational purposes only and is shared to help aspiring data professionals. Please don’t submit this work as your own in graded assessments—let’s keep it ethical!
✨ I’m always open to networking, collaboration, or sharing insights ✨
Don’t be shy — connect with me on LinkedIn! 👋
