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Google Data Analytics and Science Program (Basic-to-Advance in 12 months)

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  • 29 Sections
  • 246 Lessons
  • 250h Duration
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Course 1: Introduction
13 Lessons
  1. Welcome to the Google Data Analytics Certificate
  2. Introduction to the course
  3. Data analytics in everyday life
  4. Cassie: Dimensions of data analytics
  5. What is the data ecosystem?
  6. How data informs better decisions
  7. Discover data skill sets
  8. Key data analyst skills
  9. All about thinking analytically
  10. Explore core analytical skills
  11. Data drives successful outcomes
  12. Real world data magic
  13. What to expect moving forward
Course 1: Welcome to the wonderful world of data
1 Lesson
  1. Intro to Spreadsheets, Databases, and Query Languages
Course 1: Set up your data Analytic tools!
6 Lessons
  1. The ins and outs of core data tools
  2. Spreadsheets (Columns and rows and cells)
  3. SQL in action
  4. Angie: Everyday struggles when learning new skills
  5. Becoming a data viz whiz
  6. Lilah: The power of a visualization
Course 1: Being a data professional
11 Lessons
  1. Let's get down to business
  2. The job of a data analyst
  3. Joey Path to becoming a data analyst
  4. Tony: Supporting careers in data analytics
  5. The power of data in business
  6. Rachel: Data detectives
  7. Understand data and fairness
  8. Alex: Fair and ethical data decisions
  9. Data analysts in different industries
  10. Samah: Interview best practices
  11. Congrats on finishing these foundational course. NEXT!
Course 2: Ask Effective Questions as an Analyst
8 Lessons
  1. Introduction to problem-solving and effective questioning
  2. Data in action
  3. Nikki: The data process works
  4. Common problem types
  5. Continue exploring business applications (Problems in the real world)
  6. Anmol: From hypothesis to outcome
  7. SMART questions
  8. Evan: Data opens doors
Course 2: Make Data-driven Decisions
6 Lessons
  1. Data and decisions
  2. How data empowers decisions
  3. Qualitative and quantitative data
  4. The big reveal: Sharing your findings
  5. Data versus metrics
  6. Mathematical thinking
Course 2: Spreadsheets
9 Lessons
  1. The amazing spreadsheet
  2. Get to work with spreadsheets
  3. Basic spreadsheet tasks
  4. Formulas for success
  5. Spreadsheet errors and fixes
  6. Functions 101
  7. Before solving a problem, understand it
  8. Scope of work and structured thinking
  9. Staying objective
Course 2: Stakeholders Management
14 Lessons
  1. Communicating with your team
  2. Balance needs and expectations across your team
  3. Focus on what matters
  4. Clear communication is key
  5. Tips for effective communication
  6. Balancing expectations and realistic project goals
  7. Sarah: How to communicate with stakeholders
  8. The data tradeoff: Speed versus accuracy
  9. Think about your process and outcome
  10. Meeting best practices
  11. Ximena: Joining a new team
  12. From conflict to collaboration
  13. Nathan: From the U.S. Marine Corps to data analytics
  14. Course 2 Wrap UP!
Course 3: Data for Exploration (Data types and structures)
9 Lessons
  1. Introduction to data exploration
  2. Fascinating data insights
  3. Data collection in our world
  4. Determining what data to collect
  5. Discover data formats
  6. Understanding structured data
  7. Know the type of data you're working with
  8. Data table components
  9. Meet wide and long data
Course 3: Data for Exploration (Data Responsibility)
12 Lessons
  1. Introduction to bias, credibility, privacy, and ethics
  2. Bias: From questions to conclusions
  3. Biased and unbiased data
  4. Understanding bias in data
  5. Identify good data sources
  6. What is "bad" data?
  7. Essential data ethics
  8. The importance of data ethics
  9. Data Privacy
  10. The ethical use of data
  11. Features of open data
  12. Steps for ethical data use
Course 3: Data for Exploration (Database Essentials)
12 Lessons
  1. All about databases
  2. Database features and components
  3. Exploring metadata
  4. Using metadata as an analyst
  5. Metadata management
  6. Fun with metadata
  7. Places to find data as an Analyst
  8. Importing data from spreadsheets and databases
  9. Sort and filter to focus on relevant data
  10. Setting up BigQuery, including sandbox and billing options
  11. How to use BigQuery
  12. BigQuery in action
Course 3: Data for Exploration (Organize and Protect Data)
4 Lessons
  1. Feel confident in your data
  2. Best Data Organization Practices
  3. All about file naming
  4. Security features in spreadsheets
Course 3: Data for Exploration (Engage in the Data Coimmunity)
7 Lessons
  1. Managing your presence as a data analyst
  2. Why an online presence is important
  3. Tips for enhancing your online presence
  4. Networking know-how
  5. Benefits of mentorship
  6. Rachel: Mentors are key
  7. Congratulations! Course wrap-up
Course 4: Process Data from Dirty to Clean (The Importance of data integrity)
8 Lessons
  1. Introduction to focus on integrity
  2. Why data integrity is important
  3. Balancing objectives with data integrity
  4. Dealing with insufficient data
  5. The importance of sample size
  6. Using statistical power
  7. Determine the best sample size
  8. Evaluate the reliability of your data
Course 4: Process Data from Dirty to Clean (Clean data for more accurate insights)
10 Lessons
  1. Clean it up!
  2. Why data cleaning is critical
  3. Angie: Why I love cleaning data
  4. Defining dirty data
  5. Recognize and remedy dirty data
  6. Data-cleaning tools and techniques
  7. Data-cleaning features in spreadsheets
  8. Optimize the data-cleaning process
  9. Different data perspectives
  10. Even more data-cleaning techniques
Course 4: Process Data from Dirty to Clean (Data Cleaning With SQL)
9 Lessons
  1. Using SQL to clean data
  2. For the love of SQL
  3. Understanding SQL capabilities
  4. Spreadsheets versus SQL
  5. Widely used SQL queries
  6. Having fun with SQL
  7. Cleaning string variables using SQL
  8. Advanced data-cleaning functions, part 1
  9. Advanced data-cleaning functions, part 2
Course 4: Process Data from Dirty to Clean (Verify and report on cleaning results)
6 Lessons
  1. Verifying and reporting results
  2. Cleaning and your data expectations
  3. The final step: Verification of data cleaning
  4. Capturing cleaning changes
  5. Why documentation is important
  6. Feedback and cleaning
Course 4: Process Data from Dirty to Clean (Add data to your resume)
8 Lessons
  1. About the data-analyst hiring process
  2. The data analyst job application process
  3. Creating a resume
  4. Joseph: Black and African American inclusion in the data industry
  5. Getting Hired as a Data Analyst (The Full Class)
  6. Kate: My career path as a data analyst
  7. Where does your interest lie
  8. congratulations on finishing course 4
Course 5: Analyze Data to Answer Questions (organizing data for more effective analysis)
9 Lessons
  1. Introduction to getting organized
  2. The analysis process
  3. Ayanna - Sticking with it
  4. Always a need to organize
  5. Filter data with SQL
  6. Sort data in spreadsheets
  7. Use the SORT function in spreadsheets
  8. Emma: Journey to a meaningful career
  9. Sorting queries in SQL
Course 5: Analyze Data to Answer Questions (Format and adjust data)
10 Lessons
  1. Get started with data formatting
  2. From one type to another
  3. Data validation
  4. Conditional formatting
  5. Merge text strings to gain insights
  6. Strings in spreadsheets
  7. What to do when you get stuck
  8. Layla: All about the analyze stage
  9. Running into challenges? Not to worry!
  10. When to use which tool
Course 5: Analyze Data to Answer Questions (Aggregate data for analysis)
8 Lessons
  1. Aggregate data for analysis
  2. Preparing for VLOOKUP
  3. Identifying common VLOOKUP errors
  4. Understanding JOINS
  5. COUNT and COUNT DISTINCT
  6. Queries within queries
  7. Using subqueries to aggregate data
  8. Justin: Where data analysis takes yo
Course 5: Analyze Data to Answer Questions (Perform data calculations)
11 Lessons
  1. Data calculations
  2. Common calculation formulas
  3. Functions and conditions
  4. Composite functions
  5. Start working with pivot tables
  6. Queries and calculations
  7. Embedding simple calculations in SQL
  8. Calculations with other statements
  9. Check and recheck
  10. Temporary tables
  11. Multiple table variations
Course 6: Share Data Through the Art of Visualization (Visualize data)
12 Lessons
  1. Introduction to communicating data insights
  2. Kevin: The power's in the data viz
  3. Why data visualization matters
  4. Connect images with data
  5. A recipe for a powerful visualization
  6. Dynamic visualizations
  7. Elements of art
  8. Data visualization impact
  9. Design thinking and visualizations
  10. Headlines, subtitles, and labels
  11. Accessible visualizations
  12. Andrew: Making data accessible
Course 6: Share Data Through the Art of Visualization (Data visualizations with Tableau)
1 Lesson
  1. Creating Data Visualizations with Tableau
Course 6: Share Data Through the Art of Visualization (Crafting Stories with Data)
9 Lessons
  1. Storytelling with data
  2. Bringing ideas to life
  3. Speaking to your audience
  4. Carolyn: Data journalism
  5. Tableau dashboard basics
  6. From filters to charts
  7. Compelling presentation tips
  8. Sharing a narrative
  9. Sundas: How to manage imposter syndrome
Course 6: Share Data Through the Art of Visualization (Best Practices for Presenting Data)
12 Lessons
  1. Pulling it all together
  2. Presenting with a framework
  3. Weaving data into your presentation
  4. Brittany: Presentation skills for new data analysts
  5. Connor: Messy example of a data presentation
  6. Connor: Good example of a data presentation
  7. Proven presentation tips
  8. Present like a pro
  9. Anticipate the question
  10. Handling objections
  11. Q&A best practices
  12. Connor: Becoming an expert data translator
Course 7: Programming with R as a Data Analyst
5 Lessons
  1. Programming with R as a Data Analyst
  2. Programming Using RStudio
  3. Working with Data in R
  4. Intro to Data Visualization with R & ggplot2
  5. R Markdown with RStudio for Beginners
Advance Data Analysis Program (Data Science)
6 Lessons
  1. Data Science for Beginners | Google Advanced Data Analytics Certificate
  2. Python for Beginners
  3. Translate Data Into Insights
  4. The Vital Role of Statistics
  5. Regression Analysis: Simplify Data Relationships
  6. The Nuts and Bolts of Machine Learning
Final Lap: Begin Your Career
10 Lessons
  1. The Role of Data Professionals
  2. Two Categories of Data Careers
  3. The Fastest-Growing Career in 2023
  4. 3 Key Skills Data Professionals Need for Success
  5. High-Paying & Fast-Growing Careers in Data Analytics
  6. How To Improve Your LinkedIn Profile
  7. 4 Tips to Build Key Work Relationships
  8. Preparing For A Data Analyst Career
  9. Do You Like Customer Service? Try This Career
  10. Tips For Leading Your Team at Work

The Google Data Analytics and Science Program is a structured, 12-month learning journey designed to take you from beginner to advanced levels in data analytics and data science. This program is perfect for anyone looking to break into the world of data, transition careers, or enhance their analytical skills for professional growth.

With comprehensive modules, over 247 instructional videos, and hands-on projects, you’ll gain the practical skills needed to succeed in today’s data-driven job market.

Program Structure

1. Foundations of Data Analytics

  • Introduction to Data Analytics
  • The Data Ecosystem and Industry Trends
  • Common Data Tools and Technologies

2. Data Collection and Preparation

  • Understanding Data Sources
  • Data Cleaning and Preprocessing
  • Organizing Data for Analysis

3. Data Analysis Techniques

  • Exploratory Data Analysis (EDA)
  • Statistical Techniques for Data Analysis
  • Problem-Solving with Data

4. Data Visualization

  • Principles of Effective Data Visualization
  • Creating Dashboards in Tableau and Google Data Studio

5. Databases and SQL

  • Basics of SQL for Data Analysis
  • Writing Queries to Extract Insights

6. Advanced Data Analysis Program (Data Science)

This section introduces data science fundamentals, consisting of six specialized courses:

  • Data Science for Beginners – Learn the core concepts of data science.
  • Python for Beginners – Build programming skills for data manipulation and analysis.
  • Translate Data Into Insights – Learn how to generate actionable business insights.
  • The Vital Role of Statistics – Understand key statistical concepts used in data analysis.
  • Regression Analysis: Simplify Data Relationships – Master predictive modeling techniques.
  • The Nuts and Bolts of Machine Learning – Explore the basics of machine learning and AI.

7. Final Lap: Begin Your Career

This module consists of 10 career-focused videos to help you transition into the data profession:

  • The Role of Data Professionals
  • Two Categories of Data Careers
  • The Fastest-Growing Career in 2023
  • 3 Key Skills Data Professionals Need for Success
  • High-Paying & Fast-Growing Careers in Data Analytics
  • How To Improve Your LinkedIn Profile
  • 4 Tips to Build Key Work Relationships
  • Preparing For A Data Analyst Career
  • Do You Like Customer Service? Try This Career
  • Tips For Leading Your Team at Work

8. Real-World Project

The final module is a practical project where learners apply their skills to solve a real-world data problem. This project allows students to demonstrate their expertise to potential employers and build a strong portfolio.

Why Enroll?

✅ Structured Learning Path – Covers everything from basic analytics to advanced data science in one program.
✅ Practical & Hands-On – Work on real-world problems and gain industry-relevant experience.
✅ Career Readiness – Dedicated career module to help you land high-paying data roles.
✅ No Prior Experience Required – Beginner-friendly curriculum that grows with you.

Who Should Enroll?

  • Aspiring data analysts, data scientists, or business intelligence professionals.
  • Professionals looking to transition into data-related careers.
  • Students and graduates aiming to gain a competitive edge in the job market.

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