Google Advanced Data Analytics Professional Certificate

Learn in-demand skills like statistical analysis, Python, regression models, and machine learning in less than 6 months.

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📖 What you'll learn

• Explore the roles of data professionals within an organization.
• Create data visualizations and apply statistical methods to investigate data.
• Build regression and machine learning models to analyze and interpret data.
• Communicate insights from data analysis to stakeholders.

📈 Skills you'll gain

🏆 Endorsements and recognition

• ACE® College Credit Recommendation: Up to 9 US college credits upon completion
• FIBAA (ECTS) Certified: Earn up to 8 ECTS credits, recognized by European universities
• Google Career Certificates Employer Consortium: Access to 150+ top employers (Google, Deloitte, Target, Verizon, etc.)
• High learner satisfaction: 4.8-star rating with over 5,200 reviews, and 75% of completers report a positive career outcome within 6 months

📚 Courses and lessons

1. Foundations of Data Science

   • Understand common careers and industries that use advanced data analytics
   • Investigate the impact data analysis can have on decision-making
   • Explain how data professionals preserve data privacy and ethics
   • Develop a project plan considering roles and responsibilities of team members

2. Get Started with Python

   • Explain how Python is used by data professionals
   • Explore basic Python building blocks, including syntax and semantics
   • Understand loops, control statements, and string manipulation
   • Use data structures to store and organize data

3. Go Beyond the Numbers: Translate Data into Insights

   • Apply the exploratory data analysis (EDA) process
   • Explore the benefits of structuring and cleaning data
   • Investigate raw data using Python
   • Create data visualizations using Tableau

4. The Power of Statistics

   • Explore and summarize a dataset
   • Use probability distributions to model data
   • Conduct a hypothesis test to identify insights about data
   • Perform statistical analyses using Python

5. Regression Analysis: Simplify Complex Data Relationships

   • Investigate relationships in datasets
   • Identify regression model assumptions
   • Perform linear and logistic regression using Python
   • Practice model evaluation and interpretation

6. The Nuts and Bolts of Machine Learning

   • Identify characteristics of the different types of machine learning
   • Prepare data for machine learning models
   • Build and evaluate supervised and unsupervised learning models using Python
   • Demonstrate proper model and metric selection for a machine learning algorithm

7. Google Advanced Data Analytics Capstone

   • Examine data to identify patterns and trends
   • Build models using machine learning techniques
   • Create data visualizations
   • Explore career resources

8. Accelerate Your Job Search with AI

   • Uncover your skills and explore new career possibilities, with support from tools like Career Dreamer.
   • Keep your applications organized with Google Sheets.
   • Build a stand out resume and a step-by-step job search plan—with help from Gemini.
   • Prepare for interviews and practice responding to questions using NotebookLM and Gemini Live.

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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! 👋