课程
Introduction to Polars
- 基础技能水平
- 4.8+
- 391 条评价
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
数据处理
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 efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
数据处理
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数据工程
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数据可视化
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机器学习
机器学习
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Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
报告
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Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.
数据处理
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概率与统计
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Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
机器学习
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This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
机器学习
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Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
人工智能
人工智能
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This course explores how organizations can use custom gen AI agents to help tackle specific business challenges.
云计算
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软件开发
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概率与统计
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Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
概率与统计
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应用金融
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概率与统计
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数据准备
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应用金融
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软件开发
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机器学习
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Analyze text data in R using the tidy framework.
数据处理
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软件开发
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应用金融
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机器学习
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数据处理
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数据素养
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。