课程
Monetizing Artificial Intelligence
- 基础技能水平
- 4.7+
- 867 条评价
Explore AI and data monetization strategies, build ethical infrastructures, and align products with business goals.
人工智能
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
Explore AI and data monetization strategies, build ethical infrastructures, and align products with business goals.
人工智能
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数据处理
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Learn SQL Querying with AI by writing prompts, generating queries, and analyzing data to solve real-world problems.
数据处理
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Data storytelling is a high-demand skill that elevates analytics. Learn narrative building and visualizations in this course with a college major dataset!
数据素养
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Discover how to make better business decisions by applying practical data frameworks—no coding required.
领导力
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Learn about the power of Databricks Lakehouse and help you scale up your data engineering and machine learning skills.
数据工程
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You learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
人工智能
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Master the key concepts of data management, from life cycle stages to security and governance.
数据管理
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Learn to combine data across multiple tables to answer more complex questions with dplyr.
数据处理
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Understand the role and real-world realities of Explainable Artificial Intelligence (XAI) with this beginner friendly course.
人工智能
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In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.
软件开发
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Learn about data science for managers and businesses and how to use data to strengthen your organization.
数据素养
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Create impactful presentations faster with Gemini in Google Slides. Use AI-powered design and suggestions to build professional, engaging slides in minutes.
人工智能
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Master AWS security, governance, and cost optimization to prepare for the Cloud Practitioner certification.
云计算
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Create interactive data visualizations in Python using Plotly.
数据可视化
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Practice data storytelling using real-world examples! Communicate complex insights effectively with a dataset of certified green businesses.
数据素养
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Create and refine videos faster with Gemini in Google Vids. Use AI-powered storyboarding and content generation to produce polished videos with ease.
云计算
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Learn how to use GPT tools responsibly and confidently. Discover how these tools work and techniques for writing prompts and evaluating outputs.
人工智能
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Take your R skills up a notch by learning to write efficient, reusable functions.
软件开发
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Learn data management in Databricks with Delta Lake, including ACID transactions, schema enforcement, and security.
数据管理
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Build PowerPoint presentations with Microsoft Copilot. Turn documents into slides, generate visuals, and speaker notes.
人工智能
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Conquer NoSQL and supercharge data workflows. Learn Snowflake to work with big data, Postgres JSON for handling document data, and Redis for key-value data.
数据工程
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Learn AI governance with Collibra. Build, embed, and scale responsible AI using tools, frameworks, and MLOps workflows.
人工智能
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Explore the Databricks Lakehouse - from medallion architecture and clusters to governance, sharing, and deployment.
数据工程
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人工智能
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Collaborate with AI to make recruiting, people ops, and policy engagement faster and fairer.
人工智能
人工智能
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Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
探索性数据分析
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Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
数据准备
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This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization.
云计算
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。