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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
Course
Cloud Run functions is Googles serverless, fully-managed functions as a service (FaaS) product.
Cloud
Course
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Probability & Statistics
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Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Software Development
Course
Learn to analyze and model customer choice data in R.
Probability & Statistics
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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
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Scale and manage multi-cluster GKE environments. Master fleets, Cloud Service Mesh, identity management, CI/CD at scale, and GKE Enterprise capabilities.
Cloud
Course
Learn about creating and securing containers, and Google Kubernetes Engine for application developers.
Cloud
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Develop data pipelines with Apache Beam and Dataflow. Cover transforms, windowing, I/O connectors, schemas, state APIs, Beam SQL, and notebooks.
Cloud
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Operate Dataflow pipelines in production. Learn monitoring, logging, troubleshooting, performance tuning, CI/CD, reliability, and templates.
Cloud
Course
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Probability & Statistics
Course
Are you curious about the inner workings of the models that are behind products like Google Translate?
Artificial Intelligence
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Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Probability & Statistics
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Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Probability & Statistics
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Learn core Google Cloud networking: VPC networks, multiple NICs, Shared VPC, and network logging and monitoring.
Cloud
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Build Google Cloud security foundations with Cloud Identity, IAM, and VPC controls for isolation and logging.
Cloud
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Use Terraform to provision and manage Google Cloud resources as code, from your first config to reusable modules.
Cloud
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Harden GKE clusters, secure workload identities and secrets, and defend containers across their full lifecycle.
Cloud
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Harden Google Cloud VMs and protect critical workloads across the compute lifecycle, from boot to runtime.
Cloud
Course
Mitigate DDoS, content-related threats, and other attacks on Google Cloud using monitoring, logging, and auditing.
Cloud
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See how Gemini in Google Cloud helps network engineers plan, build, and maintain VPC networks with AI assistance.
Cloud
Course
Design scalable, secure, cost-efficient Google Cloud network architectures and build a hub-and-spoke topology.
Cloud
Course
Learn best practices for securing AI models on Google Cloud, from protecting model integrity to controlling access.
Cloud
Course
Secure Google Cloud apps with Cloud Armor, Web Security Scanner, Identity Platform MFA, and Google’s SAIF framework.
Cloud
Course
Protect Google Cloud data with IAM-only access, Sensitive Data Protection, time-bound sharing, and Cloud KMS.
Cloud
Course
Secure VPC networks with Cloud NGFW: firewall policies, secure tags, intrusion prevention, and Firewall Insights.
Cloud
Course
Master Google Cloud routing and addressing, from IP addresses, routes, and Cloud DNS to NAT and private connectivity.
Cloud
Course
Apply the Google Cloud Well-Architected Framework to design resilient, secure, cost-effective cloud architectures.
Cloud
Course
Connect on-premises and multicloud networks to Google Cloud with Cloud Interconnect, Cloud VPN, and more.
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.