课程
Intermediate Java
- 基础技能水平
- 4.8+
- 1,052 条评价
Learn to write cleaner, smarter Java code with methods, control flow, and loops.
软件开发
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 write cleaner, smarter Java code with methods, control flow, and loops.
软件开发
课程
Master Git’s advanced features to streamline data science and engineering workflows, from complex merging to large-scale project optimization.
软件开发
课程
Get started with Sigma! Learn how to build and customize simple, interactive dashboards for real-time analytics.
数据处理
课程
Build powerful multi-agent systems by applying emerging agentic design patterns in the LangGraph framework.
人工智能
课程
Master Power Pivot in Excel to help import data, create relationships, and utilize DAX. Build dynamic dashboards to uncover actionable insights.
数据处理
课程
Explore the latest techniques for running the Llama LLM locally and integrating it within your stack.
人工智能
课程
Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.
数据准备
课程
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
应用金融
课程
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
软件开发
课程
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
探索性数据分析
课程
Step right into the dynamic world of data modeling with Snowflake!
数据工程
课程
Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
软件开发
课程
Master Excel basics quickly: navigate spreadsheets, apply formulas, analyze data, and create your first charts!
数据处理
课程
Learn to perform linear and logistic regression with multiple explanatory variables.
概率与统计
课程
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
数据工程
课程
Building on your foundational Power Query in Excel knowledge, this intermediate course takes you to the next level of data transformation mastery
数据准备
课程
This course dives deeper into the Azures backbone by going into topics like containers, virtual machines and much more.
云计算
课程
Learn how to manipulate and visualize categorical data using pandas and seaborn.
数据处理
课程
Learn key object-oriented programming concepts, from basic classes and objects to advanced topics like inheritance and polymorphism.
软件开发
课程
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
数据准备
课程
Master Snowflakes three-layer architecture and build the mental model you need to work effectively in Snowflake.
课程
To understand Fabric’s main use cases, you will explore various tools in the seven Fabric experiences.
其他
人工智能
课程
Explore data ethics with this comprehensive introductory course, covering principles, AI ethics, and practical skills to ensure responsible data use.
数据素养
课程
Explore AI and data monetization strategies, build ethical infrastructures, and align products with business goals.
人工智能
课程
Learn to use Google Sheets to clean, analyze, and draw insights from data. Discover how to sort, filter, and use VLOOKUP to combine data.
数据处理
课程
Learn SQL Querying with AI by writing prompts, generating queries, and analyzing data to solve real-world problems.
数据处理
课程
Data storytelling is a high-demand skill that elevates analytics. Learn narrative building and visualizations in this course with a college major dataset!
数据素养
课程
Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.
人工智能
课程
In this course you will learn the details of linear classifiers like logistic regression and SVM.
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。