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Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Machine Learning
Course
This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Core Services.
Cloud
Course
In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud.
Cloud
Course
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, and how to get applications containerized and running in Google Cloud.
Cloud
Course
This course introduces the Cloud Run serverless platform for running applications.
Cloud
Course
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Software Development
Course
Master Snowflakes three-layer architecture and build the mental model you need to work effectively in Snowflake.
Course
Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task.
Cloud
Course
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Probability & Statistics
Course
n this Google DeepMind course you will focus on the training process for machine learning models.
Cloud
Course
Analyze data with functions, visualize it with charts, and master search, validation, and formatting in Google Sheets.
Cloud
Course
In this Google DeepMind course you will learn how to prepare text data for language models to process.
Cloud
Course
Learn to compose, send, and manage email in Gmail, organize messages with labels, and configure settings like filters and signatures.
Cloud
Course
This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
Cloud
Course
This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring.
Cloud
Course
Learn to upload, organize, share, and manage files and folders in Google Drive from any device.
Cloud
Course
Learn how to build, configure, and share Skills in Claude Code — reusable markdown instructions that Claude automatically applies to tasks at the right time.
Artificial Intelligence
Course
Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.
Cloud
Course
Learn Google Cloud essentials including computing, storage, networking, and resource management through videos and hands-on labs in this foundational course.
Cloud
Course
Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.
Cloud
Course
Learn to secure, govern, and manage Snowflake at scale. Cover RBAC, data masking, cost monitoring, Time Travel, and secure data sharing.
Data Management
Course
With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
Cloud
Course
Learn to create and edit spreadsheets in Google Sheets, work with data, build formulas, and collaborate in real time.
Cloud
Course
Learn to create, format, and collaborate on documents in real time using Google Docs, stored securely in the cloud.
Cloud
Course
Explore advanced Google Sheets features including conditional formatting, complex formulas, data validation, and referencing.
Cloud
Course
Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.
Cloud
Course
Learn how to use and create sub-agents in Claude Code to manage context, delegate tasks, and build workflows that keep your conversation clean and focused.
Artificial Intelligence
Course
Load, automate, and optimize data pipelines in Snowflake using COPY INTO, Snowpipe, streams, tasks, dynamic tables, and query performance tools.
Data Engineering
Course
Secure and monitor GKE production environments. Learn access control, logging, monitoring, CI/CD pipelines, and managed storage integration on Google Cloud.
Cloud
Course
Deploy and manage Kubernetes workloads on GKE. Cover networking, deployments, jobs, persistent storage, and data management in production environments.
Cloud
Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.
You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.
As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.
In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.
Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.
There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.
Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.
For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.
Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.