课程
Supervised Learning in R: Classification
- 中级技能水平
- 4.7+
- 138 条评价
In this course you will learn the basics of machine learning for classification.
机器学习
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
In this course you will learn the basics of machine learning for classification.
机器学习
课程
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
数据准备
课程
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
课程
Learn to work with Plain Old Java Objects, master the Collections Framework, and handle exceptions like a pro, with logging to back it all up!
软件开发
课程
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
软件开发
课程
Take your R skills up a notch by learning to write efficient, reusable functions.
软件开发
课程
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
机器学习
课程
Build and deploy scalable web apps and serverless functions in Azure while mastering security, monitoring, and automation.
云计算
课程
Create new features to improve the performance of your Machine Learning models.
机器学习
课程
Master Apache Kafka! From core concepts to advanced architecture, learn to create, manage, and troubleshoot Kafka for real-world data streaming challenges!
数据工程
课程
Learn data management in Databricks with Delta Lake, including ACID transactions, schema enforcement, and security.
数据管理
课程
Build PowerPoint presentations with Microsoft Copilot. Turn documents into slides, generate visuals, and speaker notes.
人工智能
课程
In this course, you will use T-SQL, the flavor of SQL used in Microsofts SQL Server for data analysis.
软件开发
课程
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
课程
Learn AI governance with Collibra. Build, embed, and scale responsible AI using tools, frameworks, and MLOps workflows.
人工智能
概率与统计
课程
Explore the Databricks Lakehouse - from medallion architecture and clusters to governance, sharing, and deployment.
数据工程
课程
Learn to create your own Python packages to make your code easier to use and share with others.
软件开发
课程
Enhance virtual meetings with Gemini in Google Meet. Leverage AI-driven summaries, notes, and tools to make every meeting more efficient and actionable.
人工智能
课程
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
机器学习
课程
Unlock BigQuerys power: grasp its fundamentals, execute queries, and optimize workflows for efficient data analysis.
数据工程
课程
Collaborate with AI to make recruiting, people ops, and policy engagement faster and fairer.
人工智能
人工智能
课程
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
探索性数据分析
课程
Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
数据准备
课程
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
机器学习
课程
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
课程
This course focuses on feature engineering and machine learning for time series data.
机器学习
课程
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
数据可视化
课程
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
机器学习
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。