数据分析

数据分析课程介绍管理和分析大规模数据的方法。您将学习数据挖掘、大数据应用以及数据产品开发,成为一名数据科学家。

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Introduction to Data Science

Introduction to Data Science

IBM
专项课程
评分为 4.6(满分 5 星)。
Excel Skills for Business

Excel Skills for Business

Macquarie University
专项课程
评分为 4.8(满分 5 星)。
Data Science

Data Science

Johns Hopkins University
专项课程
评分为 4.5(满分 5 星)。
Data Science: Foundations using R

Data Science: Foundations using R

Johns Hopkins University
专项课程
评分为 4.6(满分 5 星)。
商业分析

商业分析

University of Pennsylvania
专项课程
评分为 4.6(满分 5 星)。
Applied Data Science with Python

Applied Data Science with Python

University of Michigan
专项课程
评分为 4.5(满分 5 星)。
从 Excel 到 MySQL:商业分析技术

从 Excel 到 MySQL:商业分析技术

Duke University
专项课程
评分为 4.6(满分 5 星)。
Search Engine Optimization (SEO)

Search Engine Optimization (SEO)

University of California, Davis
专项课程
评分为 4.7(满分 5 星)。
Data Analysis and Presentation Skills: the PwC Approach

Data Analysis and Presentation Skills: the PwC Approach

PwC
专项课程
评分为 4.7(满分 5 星)。
Data Visualization with Tableau

Data Visualization with Tableau

University of California, Davis
专项课程
评分为 4.6(满分 5 星)。
AI Foundations for Everyone

AI Foundations for Everyone

IBM
专项课程
评分为 4.7(满分 5 星)。
Learn SQL Basics for Data Science

Learn SQL Basics for Data Science

University of California, Davis
专项课程
评分为 4.1(满分 5 星)。
Statistics with R

Statistics with R

Duke University
专项课程
评分为 4.5(满分 5 星)。
Accelerated Computer Science Fundamentals

Accelerated Computer Science Fundamentals

University of Illinois at Urbana-Champaign
专项课程
评分为 4.7(满分 5 星)。
Geographic Information Systems  (GIS)

Geographic Information Systems (GIS)

University of California, Davis
专项课程
评分为 4.8(满分 5 星)。
数据结构与算法

数据结构与算法

University of California San Diego
专项课程
评分为 4.6(满分 5 星)。

    关于 数据分析 的常见问题

  • Data analysis is the process of applying statistical analysis and logical techniques to extract information from data. When carried out carefully and systematically, the results of data analysis can be an invaluable complement to qualitative research in producing actionable insights for decision-making.

    If that sounds a lot like data science, you’re right! It’s a closely related field, but there are important differences. Data scientists typically come from computer science and programming backgrounds and rely on coding skills to build algorithms and analytic models to automate the processing of data at scale. Data analysts typically have backgrounds in mathematics and statistics, and frequently apply these analytic techniques to answer specific business problems - for example, a financial analyst at an investment bank.