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)
Based on program graduate survey, United States 2022
This program includes over 200 hours of instruction and hundreds of practice-based assessments, which will help you simulate real-world advanced data analytics scenarios that are critical for success in the workplace. The content is highly interactive and exclusively developed by Google employees with decades of experience in advanced data analytics and data science. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to advanced data analytics tools and platforms and key technical skills required for an advanced role.
Platforms and tools you will learn include: Jupyter Notebook, Python, Tableau
In addition to expert training and hands-on projects, you'll complete a capstone project that you can share with potential employers to showcase your new skill set. Learn concrete skills that top employers are hiring for right now.
There are 7 Courses in this Professional Certificate Specialization are as follows:
This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
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
-
Describe the functions of data analytics and data science within an organization
-
Identify tools used by data professionals
-
Explore the value of data-based roles in organizations
-
Investigate career opportunities for a data professional
-
Explain a data project workflow
-
Develop effective communication skills
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.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
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
-
Define what a programming language is and why Python is used by data scientists
-
Create Python scripts to display data and perform operations
-
Control the flow of programs using conditions and functions
-
Utilize different types of loops when performing repeated operations
-
Identify data types such as integers, floats, strings, and booleans
-
Manipulate data structures such as , lists, tuples, dictionaries, and sets
-
Import and use Python libraries such as NumPy and pandas
This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you build your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
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
-
Use Python tools to examine raw data structure and format
-
Select relevant Python libraries to clean raw data
-
Demonstrate how to transform categorical data into numerical data with Python
-
Utilize input validation skills to validate a dataset with Python
-
Identify techniques for creating accessible data visualizations with Tableau
-
Determine decisions about missing data and outliers
-
Structure and organize data by manipulating date strings
This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
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
-
Describe the use of statistics in data science
-
Use descriptive statistics to summarize and explore data
-
Calculate probability using basic rules
-
Model data with probability distributions
-
Describe the applications of different sampling methods
-
Calculate sampling distributions
This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
Investigate relationships in datasets
-
Identify regression model assumptions
-
Perform linear and logistic regression using Python
-
Practice model evaluation and interpretation
-
Explore the use of predictive models to describe variable relationships, with an emphasis on correlation
-
Determine how multiple regression builds upon simple linear regression at every step of the modeling process
-
Run and interpret one-way and two-way ANOVA tests
-
Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models
This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
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
-
Apply feature engineering techniques using Python
-
Construct a Naive Bayes model
-
Describe how unsupervised learning differs from supervised learning
-
Code a K-means algorithm in Python
-
Evaluate and optimize the results of K-means model
-
Explore decision tree models, how they work, and their advantages over other types of supervised machine learning
-
Characterize bagging in machine learning, specifically for random forest models
-
Distinguish boosting in machine learning, specifically for XGBoost models
-
Explain tuning model parameters and how they affect performance and evaluation metrics
-
Construct and interpret confidence intervals
-
Conduct hypothesis tests
This is the seventh and final course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses. During this capstone project, you'll use your new skills and knowledge to develop data-driven insights for a specific business problem.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
-
Examine data to identify patterns and trends
-
Build models using machine learning techniques
-
Create data visualizations
-
Explore career resources
-
Create and/or update your resume
-
Create and/or update your professional portfolio
-
Develop a data frame
-
Compose data visualizations
-
Use statistics to analyze and interpret data
-
Build, interpret, and evaluate regression models
-
Utilize machine learning techniques in Python