Course
Responsible AI for Developers: Fairness & Bias
- IntermediateSkill Level
- 5
- 12 reviews
This course covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices.
Cloud
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
Course
This course covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices.
Cloud
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Learn the fundamentals of valuing stocks.
Applied Finance
Course
Deploy and manage Kubernetes workloads on GKE. Cover networking, deployments, jobs, persistent storage, and data management in production environments.
Cloud
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Predict employee turnover and design retention strategies.
Machine Learning
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This course explores identity management and access control within a cloud environment, covering authentication, authorization, auditing, and more.
Cloud
Course
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
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Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.
Cloud
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Are you curious about the inner workings of the models that are behind products like Google Translate?
Artificial Intelligence
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Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Probability & Statistics
Course
This course is a thrilling mix of expert-led courses and immersive Google Cloud challenges through interactive labs.
Cloud
Course
Learn best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
Cloud
Course
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Software Development
Course
Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
Software Development
Course
Gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks.
Cloud
Course
Learn to analyze and model customer choice data in R.
Probability & Statistics
Course
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Data Manipulation
Course
Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Data Visualization
Course
Learn about Gen AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs.
Cloud
Course
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Probability & Statistics
Course
Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Data Visualization
Course
Learn about gemini CLI installation and configuration, and introduces use cases and security best practices
Cloud
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This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run.
Cloud
Course
This course reviews the essential security features of Model Armor and equips you to work with the service.
Cloud
Course
It explores practical methods and tools to implement AI privacy and safety recommended practices.
Cloud
Course
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
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Probability & Statistics
Course
Tired of launch week chaos? Plan and write a full product launch in a single session with Claude Cowork.
Artificial Intelligence
Course
You learn best practices for cloud applications, and how to select compute and data options to match your application use cases.
Cloud
Course
The course introduces the benefits of Gemini Code Assist and compares the features of the different Gemini Code Assist editions.
Cloud
Course
You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications.
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.