课程
Gen AI Apps: Transform Your Work
- 基础技能水平
- 4.8+
- 82 条评价
This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.
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。
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课程
This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.
云计算
课程
In this case study, you’ll use visualization techniques to find out what skills are most in-demand for data scientists, data analysts, and data engineers.
数据可视化
课程
Learn to secure, govern, and manage Snowflake at scale. Cover RBAC, data masking, cost monitoring, Time Travel, and secure data sharing.
数据管理
课程
You unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI.
云计算
课程
Exploring Data Transformation with Google Cloud
云计算
课程
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
数据管理
云计算
数据管理
课程
In this course youll learn to use and present logistic regression models for making predictions.
机器学习
课程
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
数据准备
课程
Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.
数据可视化
课程
Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.
人工智能
课程
Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
数据可视化
课程
You explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.
云计算
课程
Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
数据素养
课程
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
数据可视化
课程
Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.
报告
课程
Interact with a customized GPT and use your prompting skills to plan and open your restaurant.
人工智能
课程
Learn how to set up and manage your Microsoft Fabric infrastructure.
其他
课程
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
软件开发
课程
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
应用金融
课程
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
应用金融
课程
Connect data sources to your app to build a search and analysis engine. Master capabilities like deep research agents, ideation, and NotebookLM for analysis.
云计算
课程
Elevate decision-making skills with Decision Models, analysis methods, risk management, and optimization techniques.
数据素养
课程
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
数据可视化
课程
Learn Power Automate hands-on: build cloud flows with Microsoft 365 connectors, dynamic content, expressions, approvals, and Copilot AI assistance.
人工智能
课程
Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.
软件开发
课程
Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).
应用金融
云计算
课程
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
探索性数据分析
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。