课程
Google: Introduction to AI Agents
- 基础技能水平
- 4.8+
- 100 条评价
Gain an overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems.
云计算
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。
或
课程
Gain an overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems.
云计算
课程
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
应用金融
课程
Discover how Marketing Analysts use data to understand customers and drive business growth.
领导力
课程
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
其他
课程
Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
人工智能
课程
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
数据可视化
课程
Using Python and NumPy, learn the most fundamental financial concepts.
应用金融
课程
Learn the fundamentals of data visualization using Google Sheets.
数据可视化
课程
Ask data questions in plain English with Databricks Genie - build spaces, curate business language, and monitor quality.
数据工程
课程
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
数据处理
课程
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
报告
课程
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
机器学习
课程
This course explores how organizations can use custom gen AI agents to help tackle specific business challenges.
云计算
课程
Learn essential finance math skills with practical Excel exercises and real-world examples.
应用金融
课程
In this course, youll learn about the concepts of random variables, distributions, and conditioning.
概率与统计
课程
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
应用金融
课程
Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.
数据素养
课程
Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
人工智能
课程
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
云计算
课程
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
数据准备
课程
Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.
报告
课程
Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!
数据处理
课程
Load, automate, and optimize data pipelines in Snowflake using COPY INTO, Snowpipe, streams, tasks, dynamic tables, and query performance tools.
数据工程
课程
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
人工智能
课程
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
数据素养
课程
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
数据处理
课程
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
数据工程
课程
Learn key financial concepts such as capital investment, WACC, and shareholder value.
应用金融
课程
Build interactive AI apps in Sigma using user input, actions, and polished interfaces, no coding required.
报告
课程
Learn how to transform and analyze data within your Microsoft Fabric account
其他
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。