This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
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48,042 already enrolled
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(1,099 reviews)
Examine data to identify patterns and trends
Build models using machine learning techniques
Create data visualizations
Explore career resources
Add to your LinkedIn profile
7 quizzes, 1 assignment
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
To start, you’ll be provided with an overview of the optional capstone project and how it differs from the end-of-course projects. You’ll also receive helpful suggestions for successfully completing the capstone project. Finally, you'll learn how to incorporate your completed capstone project into your professional portfolio.
10 videos9 readings3 quizzes1 assignment2 ungraded labs
You’ll review data-focused career resources designed to help you effectively navigate the job market. You'll also get useful tips for polishing your resume and preparing for interviews.
10 videos7 readings4 quizzes
You’ll complete the final tasks necessary to earn your Google Advanced Data Analytics Certificate badge. Congratulations!
2 videos4 readings1 plugin
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Reviewed on Sep 13, 2023
I learned a great deal. This is a well-designed online course. I'd highly recommend it to others seeking an entry point into the data realm.
Reviewed on Sep 2, 2024
Nice and very challenging training but this knowledge very usefully for our organization to achieving target.
Reviewed on Nov 30, 2024
This course is outstanding and helpful, This is a friendly course, I learn allot about Data Science, Data Analyst after clearing this course.
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Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
