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This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
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(1,048 reviews)
(1,048 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
You’re almost there! 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. By the end of this course, you will: -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
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
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Grow with Google is an initiative that draws on Google's decades-long history of building products, platforms, and services that help people and businesses grow. We aim to help everyone – those who make up the workforce of today and the students who will drive the workforce of tomorrow – access the best of Google’s training and tools to grow their skills, careers, and businesses.
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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.
Reviewed on Sep 2, 2024
Nice and very challenging training but this knowledge very usefully for our organization to achieving target.
Reviewed on Sep 10, 2023
excelent course for developing skills on EDA process and good start on creating machine learning models that fits the data.
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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.
The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.
During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.
This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders.
The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.
You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analyti
We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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