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Google Cloud Training

Google Workspace를 위한 Gemini는 고객이 Google Workspace에서 생성형 AI 기능을 사용할 수 있도록 하는 부가기능입니다. 이 미니 학습 과정에서는 Gemini의 주요 기능을 살펴보고 이러한 기능으로 Google Sheets의 생산성과 효율성을 향상하는 방법을 알아봅니다.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops skills relevant to working with Google Workspace products
Can enhance Google Sheets's productivity and efficiency
Straightforward focus on Gemini for Google Workspace

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Reviews summary

Practical gemini for sheets productivity

According to learners, this course offers a concise and practical introduction to using Gemini within Google Sheets. Many find it highly relevant for enhancing productivity, especially for those working with data. While praised for its clear demonstrations and focus on immediate applicability, some learners suggest it could benefit from more advanced use cases or a deeper dive into Gemini's capabilities beyond basic automation, reflecting its 'mini course' nature. It's considered a strong starting point for integrating generative AI into spreadsheet workflows.
Addresses a current and relevant topic in AI and productivity.
"It's great to see a course covering such a new and exciting feature like Gemini right when it's becoming relevant."
"The content feels very current and directly addresses modern data challenges using cutting-edge AI."
"Learning about generative AI in Sheets right now is incredibly valuable for staying competitive and efficient."
A quick and easy-to-understand overview of Gemini's features.
"As a mini-course, it got straight to the point without any unnecessary fluff, making it efficient to complete."
"I appreciated how clearly the instructor demonstrated the core features, making complex ideas simple to grasp."
"It was easy to follow, even for someone relatively new to AI add-ons, providing a solid foundation."
Directly enhances efficiency in Google Sheets with AI.
"I found the techniques immediately applicable to my daily tasks in Google Sheets, really streamlining my workflow."
"This course genuinely helped me automate some repetitive data work using Gemini, which is a huge time-saver."
"It's a really useful introduction for anyone looking to boost their productivity with AI in spreadsheets and improve data handling."
Covers foundational aspects but lacks advanced scenarios.
"I wish there were more advanced examples or troubleshooting for specific, complex use cases beyond the basics."
"While great for beginners, I felt it only scratched the surface of what Gemini can truly do in real-world applications."
"The course provides a solid overview, but I'll need to seek additional resources for a deeper understanding and mastery."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Gemini in Google Sheets - 한국어 with these activities:
Review 'AI for Dummies'
Refreshes your knowledge of the basics of artificial intelligence.
Show steps
  • Read the chapters most relevant to Google Sheets and Gemini
Review basic spreadsheet concepts
Refreshes your understanding of basic spreadsheet concepts, which will help you better understand the advanced features of Google Sheets covered in this course.
Browse courses on Spreadsheets
Show steps
  • Review the basics of creating and editing spreadsheets.
  • Practice entering and formatting data in a spreadsheet.
  • Learn how to use basic formulas and functions.
Gemini Quickstart Guide
Provides a thorough hands-on walkthrough of the basics of Gemini.
Browse courses on Gemini
Show steps
  • Follow the step-by-step guide to set up and use Gemini in Google Sheets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Gemini Practice Exercises
Develops your proficiency in using Gemini's NLP capabilities.
Show steps
  • Complete the guided exercises provided by Google
  • Experiment with different use cases and scenarios
Gemini Use Case Blog Post
Applies your understanding of Gemini by creating a blog post that explores a specific use case.
Browse courses on Content Creation
Show steps
  • Identify a specific use case for Gemini in Google Sheets
  • Research and gather data on the use case
  • Write a blog post that clearly explains the use case and its benefits
Google Cloud Training Workshop
Provides in-depth, instructor-led training on advanced Gemini techniques.
Browse courses on App Development
Show steps
  • Register and attend a Google Cloud Training Workshop on Gemini
  • Participate actively in the workshop
  • Complete the hands-on exercises and projects
Gemini Innovation Challenge
Fosters creativity and problem-solving skills by challenging you to develop innovative solutions using Gemini.
Browse courses on Innovation
Show steps
  • Identify a problem or opportunity that Gemini can address
  • Develop a creative solution using Gemini
  • Submit your solution to the Gemini Innovation Challenge
Contribute to Gemini Open Source Project
Enhances your technical expertise and contributes to the Gemini community.
Show steps
  • Find an open issue or feature request for the Gemini open source project
  • Develop a fix or implement the feature
  • Submit a pull request to the Gemini open source repository

Career center

Learners who complete Gemini in Google Sheets - 한국어 will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Gemini in Google Sheets - 한국어.
Serves as the definitive engineering blueprint for building production applications on multimodal foundation models like Google Gemini, covering long-context prompting, retrieval-augmented generation, agentic tool calling, evaluation pipelines, and latency optimization.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
Teaches both developers and business builders how to structure reliable system instructions, few-shot examples, structured JSON outputs, and multimodal chains across Google Gemini and frontier LLM APIs.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
Provides a comprehensive overview of cybersecurity, covering the threats, risks, and challenges facing governments and businesses. It is written by two former Secretaries of Homeland Security and provides insights from their experience in leading the nation's cybersecurity efforts.
Provides an exceptionally practical, code-first curriculum that guides engineers from end-to-end data preprocessing and traditional regression pipelines to production deep learning models and generative neural networks.
Bridges no-code/low-code citizen development and full-stack software engineering, demonstrating how Google Gemini, GitHub Copilot, and natural-language AI assistants accelerate application prototyping, testing, and deployment.
Delivers an authoritative mathematical foundation in Bayesian inference, graphical models, and statistical learning algorithms, bridging foundational probability theory with rigorous predictive computational modeling.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Delves into Google Apps Script, specifically in the context of Google Sheets. It's crucial for users who want to automate tasks and extend Sheets' capabilities, fitting for advanced undergraduate students, graduate students, and professionals.
Illustrates with visual clarity how modern transformer foundation models represent text and multimodal inputs, perform semantic search, and power intelligent conversational and agentic applications.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
Focusing specifically on automating Google Sheets using Apps Script, this book provides practical examples and techniques for streamlining tasks. It's a valuable resource for professionals and advanced users.
Examines holistic architectural patterns for designing resilient machine learning infrastructure, addressing data pipelines, real-time feature stores, continual learning loops, and production deployment reliability.
Demystifies practical transformer model implementation through the Hugging Face ecosystem, demonstrating how to fine-tune, optimize, and deploy attention architectures across diverse natural language processing and understanding tasks.
Provides a deep dive into the technical aspects of cloud security. It covers topics such as cloud security architectures, security controls, and threat detection and response.
Provides a comprehensive overview of cloud security, covering everything from basic concepts to advanced topics such as threat detection and incident response. It is an excellent resource for anyone who wants to learn more about cloud security.
Provides a practical guide to cybersecurity for businesses. It covers a wide range of topics, including risk assessment, threat intelligence, and incident response. It is written by a cybersecurity expert with over 15 years of experience.
This guide focuses specifically on Google Sheets functions, which is crucial for deepening understanding and performing complex calculations. It's valuable for undergraduate and graduate students, as well as professionals who need to utilize the full power of Sheets' formulas.
Focuses specifically on invoking Google Gemini from Python to generate application code, automate data processing workflows, debug scripts, and prototype AI-powered services rapidly.
Provides a comprehensive overview of spreadsheet engineering for financial modeling, covering best practices, risk management, and advanced techniques.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
A comprehensive guide to learning Google Apps Script, which is essential for advanced Google Sheets users looking to automate workflows and build custom solutions. is suitable for graduate students and professionals.
Walks practitioners through architecting low-code and code-first generative AI applications using orchestration frameworks, vector databases, memory buffers, and multimodal foundation model APIs.
Beyond the basics, this book helps users deepen their understanding of Google Sheets for data analysis and productivity. It's suitable for undergraduate students and professionals looking to leverage Sheets more effectively. The book covers essential formulas and techniques for organizing and analyzing data.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Is particularly relevant for those transitioning from VBA to Google Apps Script, offering a bridge between the two scripting languages. It's valuable for professionals with a background in Excel automation who need to work with Google Sheets.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Architects end-to-end generative AI and machine learning workloads on Google Cloud, showing solutions builders how to operationalize Vertex AI, Gemini models, data pipelines, and low-code AutoML services in production.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Equips Python and JavaScript engineers to orchestrate Gemini and frontier language models with LangChain and LangGraph, covering retrieval pipelines, conversational memory, tool calling, and production evaluation.
A recent publication aimed at beginners, covering fundamental to advanced topics including data analysis and automation. is suitable for a wide audience from high school to professionals looking for an up-to-date comprehensive guide.
While not solely focused on Google Sheets, this resource likely covers using Sheets as a tool for data analysis and visualization, a key skill for undergraduate and graduate students and professionals. It would deepen understanding of applying Sheets in a data-centric context.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
Introduces core computational linguistics concepts and practical tokenization, tagging, and syntactic parsing workflows through clear Python examples, establishing strong fundamentals for language analysis.
Is an excellent starting point for anyone new to Google Workspace, including high school students and undergraduates. It provides a broad overview of the core applications like Gmail, Calendar, Docs, Sheets, and Drive with jargon-free instructions. This book is most valuable as foundational reading to gain a general understanding of the suite's capabilities.
Examines how Google's integration of Gemini foundation models into Search and AI Overviews reshapes digital communication, content discovery, brand visibility, and information retrieval strategy.
Synthesizes the five foundational tribes of machine learning into a unified conceptual framework, illuminating how symbolists, connectionists, evolutionaries, Bayesians, and analogizers approach intelligence.
Provides the enduring mathematical foundations of neural representation learning, sequence modeling, and gradient optimization that underpin modern multimodal AI systems like Google Gemini.
Teaches probability and statistics using a computational approach with Python. It's ideal for students and professionals with programming experience who want to understand statistical concepts by doing. It helps solidify understanding through hands-on application.

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