课程
DataLab with SQL
- 基础技能水平
- 4.8+
- 41 条评价
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
报告
观看专家讲师的短视频,然后在浏览器中通过互动练习实践所学内容。
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课程
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
报告
课程
Take your Julia skills to the next level with our intermediate Julia course. Learn about loops, advanced data structures, timing, and more.
软件开发
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Learn to compose, send, and manage email in Gmail, organize messages with labels, and configure settings like filters and signatures.
云计算
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Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
应用金融
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Analyze data with functions, visualize it with charts, and master search, validation, and formatting in Google Sheets.
云计算
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Learn human-centric AI orchestration. Distinguish between augmentation and automation, and balance machine efficiency with human intuition.
云计算
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This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
云计算
课程
Build, configure, and run your first AI agent using Googles Agent Development Kit (ADK). Set up environments, create agents in Python and YAML.
云计算
课程
This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring.
云计算
课程
Explore multi-agent system architecture and deployment using Googles ADK and Google Cloud infrastructure for production-grade agent applications.
云计算
课程
Learn how to prepare and organize your data for predictive analytics.
机器学习
课程
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
数据处理
课程
Explore advanced Google Sheets features including conditional formatting, complex formulas, data validation, and referencing.
云计算
课程
Deploy ADK agents to production using Vertex AI Agent Engine and Cloud Run. Add persistent cross-session memory with Memory Bank.
云计算
课程
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
数据可视化
课程
With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
云计算
课程
Learn to upload, organize, share, and manage files and folders in Google Drive from any device.
云计算
课程
Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.
云计算
课程
Learn to create, format, and collaborate on documents in real time using Google Docs, stored securely in the cloud.
云计算
课程
Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.
云计算
课程
Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.
云计算
课程
Turn a basic AI agent into a sophisticated assistant using advanced instructions, model selection, planning capabilities, and structured output.
云计算
课程
Learn to create and edit spreadsheets in Google Sheets, work with data, build formulas, and collaborate in real time.
云计算
课程
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
概率与统计
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Build stateful AI agents that maintain context and remember user preferences using session state, memory management, and personalization.
云计算
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Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
云计算
课程
Equip AI agents with tools for web search, code execution, database queries, and custom actions. Transform agents into capable assistants.
云计算
课程
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
概率与统计
数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
您需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助您按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
在专业领域,几乎每个行业都可以在某种程度上使用数据科学。医疗机构使用数据科学来检测和治疗疾病,金融公司用它来检测和预防欺诈。各种行业都将数据科学用于营销,如构建推荐系统和分析客户流失。
是的,数据科学是美国和全球增长最快的行业之一。它也是薪酬最高的职业之一。根据 Payscale 的数据,在美国,有经验的数据科学家平均收入为 97,609 美元,满意度评分为五星中的四星。
这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
是的,您需要一些 Python、R、SQL、Java 和 C/C++ 等语言的编程经验。不过,由于语法相对简单,Python 编程语言通常是新手的首选。
对于没有编程经验和/或数学背景的人来说,通常需要 7 到 12 个月的密集学习才能达到入门级数据科学家的水平。但是,重要的是要记住,仅仅学习数据科学的理论基础可能不会让您成为真正的数据科学家。
掌握数据科学基础后,您可以专攻各种领域,包括机器学习、人工智能、大数据分析、商业分析和智能、数据挖掘等。
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。