Foundations of Data Science (Coursera)

Offered by Google,
Foundations of Data Science (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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
This course is part of the Google Advanced Data Analytics Professional Certificate.

Syllabus

WEEK 1
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.

WEEK 2
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.

WEEK 3
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.

WEEK 4
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.

WEEK 5
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Sep 21st 2026
5-12 Weeks
Accounting Data Analytics with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Accounting Data Analytics with Python (Coursera)

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).

Sep 14th 2026
5-12 Weeks
Advanced R Programming (Coursera) Coursera
Johns Hopkins University

Advanced R Programming (Coursera)

This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions.

Sep 7th 2026
4 Weeks
Dataplex by Google Cloud (Coursera) Coursera
Board Infinity

Dataplex by Google Cloud (Coursera)

Welcome to "Dataplex By Google Cloud " a comprehensive course designed to provide a thorough understanding of Google Cloud Dataplex, a platform for managing, monitoring, and analyzing data across various data systems in Google Cloud. Spanning two modules, the course begins with the fundamentals of Dataplex, including its setup, configuration, and basic functionalities.

Sep 14th 2026
2 Weeks
Practical Machine Learning on H2O (Coursera) Coursera
H2O.ai

Practical Machine Learning on H2O (Coursera)

In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms.

Sep 14th 2026
5-12 Weeks
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Sep 21st 2026
4 Weeks
Getting Started with Data Analytics on AWS (Coursera) Coursera
AWS

Getting Started with Data Analytics on AWS (Coursera)

With the explosion of data collection enabled by the internet, mobile applications and transformation into the cloud, effective data analytics is turning into a critical tool in practically every domain – from academia to enterprise. In this course, learners will get an introduction to the different types of data analytics (descriptive, diagnostic, predictive and prescriptive) and dive into the basics of descriptive data analysis using data and tools in the AWS Cloud.

Sep 21st 2026
1 Week
A Crash Course in Data Science (Coursera) Coursera
Johns Hopkins University

A Crash Course in Data Science (Coursera)

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

Sep 7th 2026
1 Week
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Sep 14th 2026
3 Weeks