课程
Structural Equation Modeling with lavaan in R
- 高级技能水平
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Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
概率与统计
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
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
概率与统计
课程
Learn to set up a secure, efficient book recommendation app in Azure in this hands-on case study.
云计算
课程
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
机器学习
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Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
应用金融
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Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
应用金融
课程
Learn to easily summarize and manipulate lists using the purrr package.
软件开发
课程
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
数据可视化
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Learn to use the Bioconductor package limma for differential gene expression analysis.
概率与统计
课程
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
概率与统计
课程
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
软件开发
课程
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
机器学习
课程
Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
云计算
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Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
软件开发
课程
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
机器学习
课程
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
机器学习
课程
Learn to use the Census API to work with demographic and socioeconomic data.
探索性数据分析
云计算
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Explore HR data analysis in Tableau with this case study.
数据可视化
课程
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
机器学习
课程
Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.
人工智能
课程
In this Google DeepMind course you will discover the mechanisms of the transformer architecture.
云计算
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Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
数据处理
课程
This course will show you how to combine and merge datasets with data.table.
数据处理
课程
Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.
应用金融
课程
Learn to process sensitive information with privacy-preserving techniques.
机器学习
课程
Learn to analyze and model customer choice data in R.
概率与统计
课程
This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Core Services.
云计算
课程
This course introduces solution elements, including networks, load balancing, autoscaling, infrastructure automation and managed services.
云计算
课程
Explore GDPR through real-world cases on data rights, breaches, and compliance challenges.
数据管理
课程
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, and how to get applications containerized and running in Google Cloud.
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。