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.
Foundations of Data Science
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Foundations of Data Science
This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
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176,000 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(2,554 reviews)
Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 24 hours
Learn at your own pace
98%
Most learners liked this course
What you'll learn
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
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
19 quizzes, 1 assignment
Taught in English
See how employees at top companies are mastering in-demand skills
Build your Data Analysis expertise
This course is part of the Google Advanced Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 5 modules in this course
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.
What's included
7 videos10 readings2 quizzes3 plugins
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.
What's included
8 videos9 readings5 quizzes1 plugin
You’ll identify the skills data professionals use to analyze data. You'll also explore how data professionals collaborate with teammates.
What's included
9 videos6 readings2 quizzes1 assignment
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.
What's included
7 videos9 readings6 quizzes1 plugin
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.
What's included
4 videos11 readings4 quizzes
Instructor
Instructor ratings
4.8 (772 ratings)Offered by
Recommended if you're interested in Data Analysis
University of London
Google
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Frequently asked questions
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.