课程
Introduction to Spark SQL in Python
- 高级技能水平
- 4.4+
- 457
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
数据处理
课程
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
数据处理
课程
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
数据处理
课程
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
人工智能
课程
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
应用金融
课程
Learn how to work with streaming data using serverless technologies on AWS.
云
课程
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
概率与统计
课程
In this course you will learn to fit hierarchical models with random effects.
概率与统计
课程
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
人工智能
课程
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
人工智能
课程
Learn how to approach and win competitions on Kaggle.
机器学习
课程
This course covers everything you need to know to build a basic machine learning monitoring system in Python
机器学习
课程
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
软件开发
课程
Learn to build pipelines that stand the test of time.
机器学习
课程
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
人工智能
课程
Learn how to write recursive queries and query hierarchical data structures.
软件开发
课程
Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
软件开发
课程
Learn how to load, transform, and transcribe speech from raw audio files in Python.
数据处理
课程
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
人工智能
课程
Connect Java to PostgreSQL with JDBC. Write secure queries, manage transactions, and handle large datasets efficiently.
软件开发
课程
In this course youll learn techniques for performing statistical inference on numerical data.
概率与统计
课程
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
机器学习
课程
In this course youll learn how to leverage statistical techniques for working with categorical data.
概率与统计
课程
Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.
数据导入与清洗
课程
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
软件开发
课程
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
概率与统计
课程
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
机器学习
课程
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
概率与统计
课程
In this course youll learn how to perform inference using linear models.
概率与统计
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
Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.
概率与统计
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。