Course
Google Workspace End User: Use Functions, Formulas, and Charts in Google Sheets
- BasicSkill Level
- 4.6+
- 13 reviews
Analyze data with functions, visualize it with charts, and master search, validation, and formatting in Google Sheets.
Cloud
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
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Analyze data with functions, visualize it with charts, and master search, validation, and formatting in Google Sheets.
Cloud
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This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
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Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
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Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Probability & Statistics
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In this Google DeepMind course you will learn how to prepare text data for language models to process.
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Learn defensive programming in R to make your code more robust.
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This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring.
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Learn the fundamentals of valuing stocks.
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Learn how to prepare and organize your data for predictive analytics.
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Learn human-centric AI orchestration. Distinguish between augmentation and automation, and balance machine efficiency with human intuition.
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Use C++ to dramatically boost the performance of your R code.
Software Development
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Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
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Learn to create animated graphics and linked views entirely in R with plotly.
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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.
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Are you curious about the inner workings of the models that are behind products like Google Translate?
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Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Data Visualization
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Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.
Exploratory Data Analysis
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Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Data Manipulation
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Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Data Visualization
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Learn to optimize, scale, and test Polars data pipelines for production-ready performance.
Data Manipulation
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Explore advanced Google Sheets features including conditional formatting, complex formulas, data validation, and referencing.
Cloud
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Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
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Predict employee turnover and design retention strategies.
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Learn how to visualize big data in R using ggplot2 and trelliscopejs.
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Learn to use Googles Agent Development Kit (ADK) to build complex, production-ready AI agents with a code-first, structured development approach.
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Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
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Explore multi-agent system architecture and deployment using Googles ADK and Google Cloud infrastructure for production-grade agent applications.
Cloud
Course
Build, configure, and run your first AI agent using Googles Agent Development Kit (ADK). Set up environments, create agents in Python and YAML.
Cloud
Course
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Software Development
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.