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760 Courses

Course

Intermediate Power Automate

  • IntermediateSkill Level
  • 5
  • 1 review

Build reliable Power Automate cloud flows with triggers, branching, approvals, error handling, and production handover.

Artificial Intelligence

3 hours

Course

Predictive Analytics using Networked Data in R

  • IntermediateSkill Level
  • 4.8+
  • 32 reviews

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Probability & Statistics

4 hours

Course

Monitoring and troubleshooting AWS

  • IntermediateSkill Level
  • 5
  • 2 reviews

Monitor and troubleshoot AWS apps with Amazon CloudWatch and AWS X-Ray. Collect metrics and logs, build dashboards, set alarms, and trace requests.

Cloud

3 hours

Course

Developing applications on AWS

  • IntermediateSkill Level
  • 5
  • 1 review

Build cloud apps on AWS with API Gateway, Lambda, SQS, SNS, EventBridge, and Kinesis. Master serverless and event-driven patterns for the DVA-C02 exam.

Cloud

3 hours

Course

Automating Deployments on AWS

  • IntermediateSkill Level
  • 5
  • 1 review

Build CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy. Automate blue/green and canary releases, and define infrastructure with CloudFormation.

Cloud

3 hours

Course

Deploying Applications on AWS

  • IntermediateSkill Level
  • 5
  • 3 reviews

Deploy, secure, and operate apps on AWS with Lambda, API Gateway, Cognito, IAM, CloudWatch, and X-Ray. Hands-on prep for the DVA-C02 exam.

Cloud

3 hours

Course

Introduction to Gemini Enterprise

  • BasicSkill Level

Gemini Enteprise brings together AI agents, enterprise search, NotebookLM, and intelligent data access to solve organizational challenges.

Cloud

2 hours 15 min

Course

AI Infrastructure: Storage Options

  • IntermediateSkill Level
  • 4+
  • 1 review

Journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads.

Cloud

1 hour

Course

AI Infrastructure: Networking Techniques

  • IntermediateSkill Level

Design and deploy high-performance AI/ML solutions using Google Clouds AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine.

Cloud

1 hour

Course

AI Infrastructure: Deployment Types

  • IntermediateSkill Level

A guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud.

Cloud

1 hour 30 min

Course

AI Infrastructure: Cloud GPUs

  • IntermediateSkill Level

Well explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them.

Cloud

1 hour

Course

LLM Application Fundamentals with LangChain

  • IntermediateSkill Level
  • 4.6+
  • 155 reviews

Learn to build conversational LLM applications — with reliable structured output, persistent conversation history, and real-time streaming.

Artificial Intelligence

AI Tutor

3 hours

Course

Gemini for Application Developers

  • BasicSkill Level

You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications.

Cloud

1 hour 30 min

Course

Deploy and Scale AI Models with Cloud Run

  • BasicSkill Level

This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run.

Cloud

1 hour 15 min

Course

Create Generative AI Apps on Google Cloud

  • IntermediateSkill Level

Learn about Gen AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs.

Cloud

4 hours

FAQs

What is data science?

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.

How can I learn data science?

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.

What skills are required for data science?

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.

What can I use data science for?

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.

Is data science a good career?

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.

Is it difficult to become a data scientist?

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.

Does data science require coding?

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.

How long does it take to become a data scientist?

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.

What topics can I study within data science?

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.

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