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Google Career Certificates

Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹

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Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹

Advanced data professionals are responsible for collecting, analyzing, and interpreting extremely large amounts of data. These jobs require manipulating large data sets and using advanced analytics including machine learning, predictive modeling, and experimental design.

This certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the Google Data Analytics Certificate or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring Jupyter Notebook, Python, and Tableau.

After seven courses, you’ll be prepared for jobs like senior data analyst, junior data scientist, data science analyst, and more. At under 10 hours a week, the certificate program can be completed in less than six months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.

75% of certificate graduates report a positive career outcome (e.g., new job, promo or raise) within six months of completion2

¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022)

2Based on program graduate survey, United States 2022

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What's inside

Seven courses

Foundations of Data Science

This first course in the Google Advanced Data Analytics Certificate introduces the role of data professionals and the PACE project workflow. Google employees guide learners through hands-on activities and share real-world examples.

Get Started with Python

This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.

Go Beyond the Numbers: Translate Data into Insights

This course teaches how to find and tell the story within data. You'll discover how data professionals use storytelling to better understand their data and communicate key insights. You'll also practice exploratory data analysis and learn how to create effective data visualizations.

The Power of Statistics

This course explores how data professionals use statistics to analyze data and gain insights. Key concepts include descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn to use Python for statistical analysis and communicate findings.

Regression Analysis: Simplify Complex Data Relationships

This course explores regression analysis, a technique used to discover relationships between variables in a dataset. You'll learn about linear regression, ANOVA, and logistic regression, and how to use them to solve business problems. By the end, you'll be able to build predictive models and interpret their results.

The Nuts and Bolts of Machine Learning

This course explores machine learning, where algorithms and statistics are used to teach computer systems to discover patterns in data. You'll focus on supervised and unsupervised learning, applying different models to business problems. You'll also learn about Naive Bayes, decision trees, random forests, and more.

Google Advanced Data Analytics Capstone

This final course of the Google Advanced Data Analytics Certificate includes an optional capstone project. You'll use your skills to develop data-driven insights for a specific business problem.

Learning objectives

  • 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

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