课程
Supervised Learning with scikit-learn
- 中级技能水平
- 4.6+
- 18.8K
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
机器学习
课程
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
机器学习
课程
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
概率与统计
课程
Learn to combine data from multiple tables by joining data together using pandas.
数据处理
课程
Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.
人工智能
课程
Learn how to explore whats available in a database: the tables, relationships between them, and data stored in them.
探索性数据分析
课程
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
探索性数据分析
课程
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
软件开发
课程
You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.
数据处理
课程
Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.
人工智能
课程
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
数据工程
课程
Dive into the exciting world of APIs as we introduce you to the basics of consuming and working with Web APIs using Python.
软件开发
课程
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
数据准备
课程
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
机器学习
课程
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
数据处理
课程
Learn how to work with Claude using the Anthropic API to solve real-world tasks and build AI-powered applications.
人工智能
数据处理
课程
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
概率与统计
课程
Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.
软件开发
课程
This course will take you from Snowflakes foundational architecture to mastering advanced SnowSQL techniques.
数据工程
课程
Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.
数据处理
课程
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
概率与统计
课程
Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
数据可视化
课程
This introductory and conceptual course will help you understand the fundamentals of data warehousing.
数据工程
课程
This course introduces dbt for data modeling, transformations, testing, and building documentation.
数据工程
课程
Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.
数据处理
课程
Continue your data visualization journey where youll learn practical techniques for incorporating DAX measures and progressive disclosure in your reports.
数据可视化
课程
Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.
数据处理
课程
Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.
数据工程
课程
Discover the fundamental concepts of object-oriented programming (OOP), building custom classes and objects!
软件开发
课程
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
软件开发
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。
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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。
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