https://github.com/jhermienpaul/google-data-analytics-program
Hands-on learning materials from the 8-course Google Data Analytics Professional Certificate program, covering foundational data skills, tools, and real-world business problem-solving
https://github.com/jhermienpaul/google-data-analytics-program
bigquery dashboard data-analysis data-analytics data-modeling data-storytelling data-visualization data-wrangling descriptive-analytics diagnostic-analytics etl-pipeline r-programming rstudio sql tableau
Last synced: 10 months ago
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Hands-on learning materials from the 8-course Google Data Analytics Professional Certificate program, covering foundational data skills, tools, and real-world business problem-solving
- Host: GitHub
- URL: https://github.com/jhermienpaul/google-data-analytics-program
- Owner: jhermienpaul
- Created: 2025-07-02T08:52:22.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-07-09T10:08:02.000Z (10 months ago)
- Last Synced: 2025-07-09T10:43:10.663Z (10 months ago)
- Topics: bigquery, dashboard, data-analysis, data-analytics, data-modeling, data-storytelling, data-visualization, data-wrangling, descriptive-analytics, diagnostic-analytics, etl-pipeline, r-programming, rstudio, sql, tableau
- Homepage: https://www.coursera.org/professional-certificates/google-data-analytics
- Size: 79.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Google Data Analytics Professional Certificate
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.
[](https://www.credly.com/users/jhermienpaul/badges)
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### 📖 What you'll learn
- 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
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### 📈 Skills you'll gain
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### 🏆 Endorsements and recognition
- **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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### 📚 Courses and lessons
1. **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.
2. **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.
3. **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.
4. **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.
5. **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.
6. **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.
7. **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.
8. **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.
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### 🚀 How to use this repo
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!
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✨ I’m always open to networking, collaboration, or sharing insights ✨
Don’t be shy — connect with me on LinkedIn! 👋
