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771 Courses

Course

Serverless Data Processing with Dataflow: Operations

  • AdvancedSkill Level
  • 4.8+
  • 7 reviews

Operate Dataflow pipelines in production. Learn monitoring, logging, troubleshooting, performance tuning, CI/CD, reliability, and templates.

Cloud

4 hours 13 min

Course

Probability Puzzles in R

  • BasicSkill Level
  • 4.8+
  • 66 reviews

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

Probability & Statistics

4 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

Deploying Applications on AWS

  • IntermediateSkill Level
  • 5
  • 5 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

Case Studies: Network Analysis in R

  • BasicSkill Level
  • 4.7+
  • 44 reviews

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Probability & Statistics

4 hours

Course

Automating Deployments on AWS

  • IntermediateSkill Level
  • 5
  • 2 reviews

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

Google: Introduction to Generative AI

  • BasicSkill Level
  • 5
  • 1 review

This is an introductory level course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods.

Cloud

45 min

Course

AI Infrastructure: Networking Techniques

  • IntermediateSkill Level
  • 5
  • 1 review

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

Cloud

1 hour

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

Gemini for end-to-end SDLC

  • IntermediateSkill Level

With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data.

Cloud

1 hour 30 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

Production Machine Learning Systems

  • IntermediateSkill Level

Learn how to implement the various flavors of ML: static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.

Cloud

16 hours

Course

Deploy and Scale AI Models with Cloud Run

  • IntermediateSkill 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

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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