Google Data Analytics 专业证书 | Coursera

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Get on the fast track to a career in Data Analytics.
In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.
位教师：Google Career Certificates
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来自此计划中课程的 182,962 条评论
来自此计划中课程的 182,962 条评论
Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job
Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, Python, Tableau)
Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and Python
Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms
Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are over 270,000 open jobs in data analytics with a median entry-level salary of $97,000 in the U.S.¹
Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.
Over nine courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.
Upon completion, you can directly apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, Verizon, and of course, Google.
75% of certificate graduates report a positive career outcome (e.g., new job, promotion, or raise) within six months of completion²
¹Lightcast™ US Job Postings (2025: Jan. 1, 2025 - Dec. 31, 2025)
²Based on program graduate survey, United States 2022
This program includes over 180 hours of instruction and hundreds of practice-based assessments, which will help you simulate real-world 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 data analytics. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to analysis tools and platforms and key analytical skills required for an entry-level job.
Skills you’ll gain will include: Data cleaning, problem solving, critical thinking, data ethics, and data visualization
Platforms and tools you will learn include: Presentations, Spreadsheets, SQL, Tableau and Python
In addition to expert training and hands-on projects, you'll complete a case study that you can share with potential employers to showcase your new skill set. Learn concrete skills that top employers are hiring for right now.
Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems.
Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking.
Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics.
Describe the role of a data analyst with specific reference to jobs.
Explain how the problem-solving road map applies to typical analysis scenarios.
Discuss the use of data in the decision-making process.
Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.
Describe the key ideas associated with structured thinking.
Explain what factors to consider when making decisions about data collection.
Discuss the difference between biased and unbiased data.
Describe databases with references to their functions and components.
Describe best practices for organizing data.
Define different types of data integrity and identify risks to data integrity.
Apply basic SQL functions to clean string variables in a database.
Develop basic SQL queries for use on databases.
Describe the process of verifying data cleaning results.
Discuss the importance of organizing your data before analysis by using sorts and filters.
Convert and format data.