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Google Career Certificates

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.

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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.

By the end of this course, you will:

-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

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What's inside

Syllabus

Introduction to data science concepts
You’ll begin with an introduction to the Google Advanced Data Analytics Certificate. Then, you'll explore the history of data science and ways that data science helps solve problems today.
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The impact of data today
Now that you’re more familiar with the history of data science, you’re ready to explore today’s data career space. You’ll learn more about how data professionals manage and analyze their data, as well as how data-driven insights can help organizations.
Your career as a data professional
You’ll identify the skills data professionals use to analyze data. You'll also explore how data professionals collaborate with teammates.
Data applications and workflow
You’ll learn about the PACE (Plan, Analyze, Construct, Execute) project workflow and how to organize a data project. You’ll also learn how to communicate effectively with teammates and stakeholders.
Course 1 end-of-course project
You’ll complete an end-of-course project, gaining an opportunity to apply your new data skills and knowledge from Course 1 to a workplace scenario, and practice solving a business problem.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops foundational knowledge and skills for entry-level data science and advanced-level data analytics roles
Taught by Google employees who work in the field, offering real-world insights and expertise
Prepares learners for data science and advanced data analytics jobs, with seven courses covering essential skills
Builds on the foundational knowledge and skills from the Google Data Analytics Certificate
Requires prior knowledge of analytical principles, skills, and tools
Provides hands-on activities, examples, and skill-building exercises

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Data Science with these activities:
Build a Data Analytics Resource Hub
Compile a comprehensive list of data analytics tools, resources, and articles. This will serve as a valuable reference guide for your learning and career growth.
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  • Search for relevant tools and resources online.
  • Categorize and organize the resources based on their purpose.
  • Share your resource hub with classmates and colleagues.
  • Regularly update the hub with new findings and industry trends.
Develop a Data Analytics Project Proposal
Create a project proposal that outlines a data analytics project you will complete throughout the course. This will help you focus your learning and apply the skills you acquire.
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  • Identify a business problem to address.
  • Research relevant data sources and methods.
  • Develop a plan for data collection and analysis.
  • Create a timeline for project completion.
Form a Study Group with Classmates
Connect with classmates to form a study group. Engage in discussions, share insights, and support each other's learning journeys.
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  • Reach out to classmates through online platforms or forums.
  • Establish regular meeting times and a communication channel.
  • Prepare discussion topics and activities for group sessions.
  • Actively participate in discussions and share your perspectives.
Five other activities
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Explore Data Analytics with Google Developers Courses
Supplement your learning with Google Developers courses specifically tailored to data analytics. These courses provide hands-on experience and deepen your understanding of key concepts.
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  • Identify relevant courses on the Google Developers platform.
  • Select courses that align with your learning objectives.
  • Complete the courses at your own pace.
Develop a Data Storytelling Presentation
Create a data storytelling presentation that effectively communicates the insights and findings from a data analysis project. This will enhance your ability to present complex data in a compelling and actionable way.
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  • Identify the key insights and findings from your data analysis.
  • Develop a clear and concise narrative.
  • Use appropriate data visualizations to support your story.
  • Practice your presentation to ensure clarity and impact.
Mentor Junior Data Analysts
Share your knowledge and support the growth of aspiring data analysts by becoming a mentor. This will reinforce your understanding of data analytics concepts and develop your communication and leadership skills.
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  • Connect with junior data analysts through online platforms or your network.
  • Offer guidance and support on technical and career-related topics.
  • Provide constructive feedback and encourage their growth journey.
  • Attend industry events and workshops with your mentees.
Participate in Data Analytics Competitions
Challenge yourself by participating in data analytics competitions. These events provide a platform to test your skills, learn from others, and network with industry professionals.
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  • Identify relevant data analytics competitions online.
  • Form a team or participate individually.
  • Analyze the competition dataset and develop a solution.
  • Submit your solution and track your progress.
Contribute to Open-Source Data Analytics Projects
Gain practical experience and contribute to the data analytics community by participating in open-source projects. This will enhance your coding skills and expose you to real-world challenges.
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  • Identify open-source data analytics projects on platforms like GitHub.
  • Review the project documentation and contribute code.
  • Discuss your contributions with the project maintainers.
  • Attend online or offline meetups for open-source enthusiasts.

Career center

Learners who complete Foundations of Data Science will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist uses statistical and programming techniques to extract knowledge from data. They use this knowledge to make recommendations for business decisions, solve problems, and develop products and services. This course may be useful for those interested in a career as a Data Scientist because it provides a foundation in data science concepts, data analysis, and communication skills.
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. They use statistical and programming techniques to train and evaluate models, and they work with data scientists and other engineers to integrate models into products and services. This course may be useful for those interested in a career as a Machine Learning Engineer because it provides a foundation in data science concepts, machine learning, and communication skills.
Business Analyst
A Business Analyst helps businesses understand their data and make better decisions. They work with data scientists and other analysts to identify business problems, develop solutions, and communicate findings to stakeholders. This course may be useful for those interested in a career as a Business Analyst because it provides a foundation in data science concepts, data analysis, and communication skills.
Data Visualization Specialist
A Data Visualization Specialist designs and creates visualizations to communicate data insights to stakeholders. They work with data scientists and other analysts to identify the most effective ways to present data, and they use visualization tools to create clear and compelling visuals. This course may be useful for those interested in a career as a Data Visualization Specialist because it provides a foundation in data science concepts, data visualization, and communication skills.
Data Warehouse Architect
A Data Warehouse Architect designs and builds data warehouses to store and manage data for analysis. They work with data scientists and other engineers to ensure that data is available and accessible for analysis. This course may be useful for those interested in a career as a Data Warehouse Architect because it provides a foundation in data science concepts, data management, and communication skills.
Data Governance Analyst
A Data Governance Analyst helps businesses manage their data and ensure that it is used in a consistent and ethical manner. They work with data scientists and other analysts to develop data governance policies and procedures, and they monitor data usage to ensure compliance. This course may be useful for those interested in a career as a Data Governance Analyst because it provides a foundation in data science concepts, data management, and communication skills.
Data Privacy Analyst
A Data Privacy Analyst helps businesses protect the privacy of their data. They work with data scientists and other analysts to develop data privacy policies and procedures, and they monitor data usage to ensure compliance. This course may be useful for those interested in a career as a Data Privacy Analyst because it provides a foundation in data science concepts, data privacy, and communication skills.
Data Ethics Officer
A Data Ethics Officer helps businesses use data in a responsible and ethical manner. They work with data scientists and other analysts to develop data ethics policies and procedures, and they monitor data usage to ensure compliance. This course may be useful for those interested in a career as a Data Ethics Officer because it provides a foundation in data science concepts, data ethics, and communication skills.
Data Analyst
A Data Analyst collects, cleans, and analyzes data to help businesses make better decisions. They use statistical and programming techniques to identify trends, patterns, and relationships in data. This course may be useful for those interested in a career as a Data Analyst because it provides a foundation in data science concepts, data analysis, and communication skills.
Data Engineer
A Data Engineer designs and builds systems to collect, store, and process data. They work with data scientists and other engineers to ensure that data is available and accessible for analysis. This course may be useful for those interested in a career as a Data Engineer because it provides a foundation in data science concepts, data management, and communication skills.
Operations Research Analyst
An Operations Research Analyst uses data to improve the efficiency of business operations. They use statistical and programming techniques to analyze data, and they make recommendations for improving processes, reducing costs, and increasing productivity. This course may be useful for those interested in a career as an Operations Research Analyst because it provides a foundation in data science concepts, data analysis, and communication skills.
Statistician
A Statistician collects and analyzes data to make inferences about a population. They use statistical techniques to design studies, collect data, and analyze results. This course may be useful for those interested in a career as a Statistician because it provides a foundation in data science concepts, data analysis, and communication skills.
Market Research Analyst
A Market Research Analyst collects and analyzes data to understand consumer behavior. They use statistical and programming techniques to identify trends and patterns in data, and they make recommendations for marketing campaigns and product development. This course may be useful for those interested in a career as a Market Research Analyst because it provides a foundation in data science concepts, data analysis, and communication skills.
Financial Analyst
A Financial Analyst uses data to make investment decisions. They use statistical and programming techniques to analyze financial data, and they make recommendations for buying, selling, or holding stocks, bonds, and other financial instruments. This course may be useful for those interested in a career as a Financial Analyst because it provides a foundation in data science concepts, data analysis, and communication skills.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They use programming languages and software development tools to create software applications that meet the needs of users. This course may be useful for those interested in a career as a Software Engineer because it provides a foundation in data science concepts, programming, and communication skills.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Foundations of Data Science.
Good introduction to the field of data science, providing a high-level overview of the various techniques and tools used in the field. It is also a good resource for business professionals who want to learn more about how data science can be used to improve their decision-making.
Introduces readers to the basics of data analysis in Python. It covers how to load, clean, transform, and analyze data using Python's pandas and NumPy libraries.
This comprehensive introduction to R, widely used by statisticians, analysts, and data scientists. It provides a solid foundation in data science concepts and techniques for working with data in R.
Teaches readers how to build a data science project from scratch. It covers the entire data science workflow, from data collection to model deployment.
Provides a practical introduction to machine learning. It covers a wide range of machine learning algorithms and techniques, using Python's scikit-learn, Keras, and TensorFlow libraries.
Introduces readers to the fundamentals of deep learning. It covers the theory and implementation of deep learning models using Python's Keras library.
Provides a practical introduction to data visualization. It covers the principles and techniques of data visualization, including how to choose the right type of chart for your data and how to design effective visualizations.
Provides a comprehensive overview of data mining, covering a wide range of data mining algorithms and techniques. It good resource for those who want to learn about the different types of data mining algorithms and how they can be used to solve real-world problems.
Provides a comprehensive introduction to statistical inference. It covers the foundational concepts and methods of statistical inference, making it a valuable resource for those who want to learn about the theory behind statistical inference.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers the fundamental concepts and algorithms of information theory, making it a valuable resource for those who want to learn about the theory behind information theory.
Is commonly used as a textbook for graduate-level courses in convex optimization. It provides a comprehensive introduction to convex optimization, covering the theory, algorithms, and applications of convex optimization.
Provides a comprehensive introduction to deep learning. It covers the theory, algorithms, and applications of deep learning, making it a valuable resource for those who want to learn about the theory behind deep learning.

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