课程
Introduction to Python
- 基础技能水平
- 4.6+
- 136.8K
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
软件开发
课程
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
软件开发
课程
Master the Power BI basics and learn to use the data visualization software to build impactful reports.
数据可视化
课程
Learn how to write effective prompts with ChatGPT to apply in your workflow today.
人工智能
课程
Learn the fundamentals of AI agents, their components, and real-world use—no coding required.
人工智能
课程
Learn how to create and query relational databases using SQL in just two hours.
数据处理
课程
Explore AI ethics focusing on principles, fairness, bias reduction, and trust in AI design.
人工智能
课程
Explore what AI is and how to use it responsibly for smarter, more productive work!
人工智能
课程
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
软件开发
课程
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
软件开发
数据素养
课程
Start your journey developing AI-powered applications with the OpenAI API. Learn about the functionality that underpins popular AI applications like ChatGPT.
人工智能
课程
Master the Excel basics and learn to use this spreadsheet tool to conduct impactful analysis.
数据处理
课程
Accompanied at every step with hands-on practice queries, this course teaches you everything you need to know to analyze data using your own SQL code today!
数据处理
课程
Level up your SQL knowledge and learn to join tables together, apply relational set theory, and work with subqueries.
数据处理
课程
Master the fundamentals of programming in Python. No prior knowledge required!
软件开发
课程
A non-coding introduction to cloud computing, covering key concepts, terminology, and tools.
云
课程
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
数据处理
课程
Learn the basic concepts of Artificial Intelligence, such as machine learning, deep learning, NLP, generative AI, and more.
人工智能
课程
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
数据工程
课程
Learn how to use ChatGPT. Discover best practices for writing prompts and explore common business use cases for the powerful AI tool.
人工智能
课程
Gain an introduction to data in this hands-on course. Learn the basics of data types and structures, the DIKW framework, data ethics and more.
数据素养
课程
Data is all around us, which makes data literacy an essential life skill.
数据素养
课程
Enhance your Power BI knowledge, by learning the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.
数据处理
课程
Learn how to analyze data with PivotTables and intermediate logical functions before moving on to tools such as what-if analysis and forecasting.
报告与呈现
课程
An introduction to machine learning with no coding involved.
机器学习
课程
Discover the fundamentals of Git for version control in your software and data projects.
软件开发
课程
Power BI is a powerful data visualization tool that can be used in reports and dashboards.
数据可视化
课程
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
数据处理
课程
Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.
人工智能
课程
Start your Tableau journey with our Introduction to Tableau course. Discover Tableau basics such as its features and dashboards.
数据可视化
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。
你需要学习 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你按自己的节奏学习。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,你需要一些 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你成为真正的数据科学家。
掌握数据科学基础后,你可以专攻各种领域,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。