课程
Introduction to AI Agents
- 基础技能水平
- 4.7+
- 39.8K
Learn the fundamentals of AI agents, their components, and real-world use—no coding required.
人工智能
课程
Learn the fundamentals of AI agents, their components, and real-world use—no coding required.
人工智能
课程
Explore AI ethics focusing on principles, fairness, bias reduction, and trust in AI design.
人工智能
课程
Explore what AI is and how to use it responsibly for smarter, more productive work!
人工智能
数据素养
课程
A non-coding introduction to cloud computing, covering key concepts, terminology, and tools.
云
课程
Learn the basic concepts of Artificial Intelligence, such as machine learning, deep learning, NLP, generative AI, and more.
人工智能
课程
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
数据工程
课程
Gain an introduction to data in this hands-on course. Learn the basics of data types and structures, the DIKW framework, data ethics and more.
数据素养
课程
Data is all around us, which makes data literacy an essential life skill.
数据素养
课程
An introduction to machine learning with no coding involved.
机器学习
课程
Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.
人工智能
课程
Discover how to begin responsibly leveraging generative AI. Learn how generative AI models are developed and how they will impact society moving forward.
人工智能
课程
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!
概率与统计
课程
Data-driven organizations consistently rely on insights to inspire action and drive change.
数据素养
课程
An introduction to data visualization with no coding involved.
数据可视化
课程
Learn the role Generative Artificial Intelligence plays today and will play in the future in a business environment.
人工智能
课程
Discover what it takes to scale AI agents, with a little help from frameworks like MCP and A2A.
人工智能
课程
Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.
数据素养
课程
Learn the key components of building a strong data culture within an organization.
数据素养
课程
Discover how to extract business value from AI. Learn to scope opportunities for AI, create POCs, implement solutions, and develop an AI strategy.
人工智能
课程
No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
数据素养
课程
Learn how to translate business questions to well-formed analytical questions and select the right analytical solutions.
数据素养
课程
This introductory and conceptual course will help you understand the fundamentals of data warehousing.
数据工程
课程
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
机器学习
课程
Improve data literacy skills by analyzing remote working policies.
数据素养
课程
Gain a clear understanding of data privacy principles and how to implement privacy and security processes.
数据素养
课程
Learn how to blend business, data, and AI, and set goals to drive success with an effectively scalable AI Strategy.
人工智能
课程
Learn about LLMOps from ideation to deployment, gain insights into the lifecycle and challenges, and learn how to apply these concepts to your applications.
人工智能
课程
Gain an introduction to data governance, exploring its meaning, purpose, and how to implement a data governance framework.
数据管理
课程
Learn about Large Language Models (LLMs) and how they are reshaping the business world.
人工智能
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。
你需要学习 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你按自己的节奏学习。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,你需要一些 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你成为真正的数据科学家。
掌握数据科学基础后,你可以专攻各种领域,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。