课程
Streaming Data with AWS Kinesis and Lambda
- 高级技能水平
- 4.7+
- 164 条评价
Learn how to work with streaming data using serverless technologies on AWS.
云计算
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
Learn how to work with streaming data using serverless technologies on AWS.
云计算
课程
Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
人工智能
课程
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
数据准备
课程
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
应用金融
课程
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
机器学习
课程
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
机器学习
课程
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
云计算
课程
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
数据准备
课程
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
数据工程
课程
Learn how to load, transform, and transcribe speech from raw audio files in Python.
数据处理
课程
Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.
数据处理
课程
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
数据素养
课程
Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.
报告
课程
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
人工智能
课程
Learn how to visualize time series in R, then practice with a stock-picking case study.
数据可视化
课程
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
机器学习
课程
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
软件开发
课程
Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.
机器学习
课程
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
概率与统计
课程
Create multi-modal systems using OpenAIs text and audio models, including an end-to-end customer support chatbot!
人工智能
课程
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
人工智能
课程
Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.
软件开发
课程
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
云计算
课程
Visualize seasonality, trends and other patterns in your time series data.
数据可视化
课程
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
数据处理
课程
Learn key financial concepts such as capital investment, WACC, and shareholder value.
应用金融
课程
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
数据可视化
课程
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
数据工程
课程
Build interactive AI apps in Sigma using user input, actions, and polished interfaces, no coding required.
报告
课程
Learn how to transform and analyze data within your Microsoft Fabric account
其他
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。