课程
Intermediate Data Visualization with ggplot2
- 中级技能水平
- 4.7+
- 927 条评价
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
数据可视化
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 to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
数据可视化
课程
Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!
软件开发
课程
Learn how to use FastAPI to develop APIs that support AI models, built to meet real-world demands.
人工智能
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Master Azure Management and Governance with our comprehensive course, ideal for data professionals seeking cloud expertise.
云计算
课程
Master data manipulation and analysis techniques such as CASE statements, subqueries, and CTEs in Snowflake.
数据处理
课程
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
软件开发
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Learn about string manipulation and become a master at using regular expressions.
软件开发
课程
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
机器学习
课程
Discover how AI can take your consulting work to the next level! Research, analyze, and communicate more productively and effectively.
人工智能
课程
Master Microsoft Copilot in Word to write faster, understand documents instantly, and collaborate more effectively.
人工智能
课程
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
概率与统计
课程
Transform almost any dataset into a tidy format to make analysis easier.
数据处理
课程
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
数据准备
课程
Master AI for marketing to plan smarter campaigns, create quality content, and build custom AI agents.
人工智能
课程
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
数据准备
课程
Learn to manipulate and analyze flexibly structured data with MongoDB.
数据工程
课程
Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.
报告
数据准备
课程
Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.
数据可视化
课程
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
机器学习
课程
Advance your Java skills by learning to handle files, process data, and build clean, reusable code using real-world techniques.
软件开发
课程
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
数据处理
课程
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
应用金融
课程
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
机器学习
课程
Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.
数据素养
课程
Learn the core techniques necessary to extract meaningful insights from time series data.
概率与统计
课程
Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
数据工程
课程
This course helps your preparation for the Associate Cloud Engineer exam, learn about the Google Cloud domains in the exam and create a study plan.
云计算
课程
In this course, youll learn the basics of relational databases and how to interact with them.
数据处理
课程
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
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。