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。

...
筛选依据:
393 个结果
排序方式:
What is Data Science?

What is Data Science?

IBM
课程
评分为 4.7(满分 5 星)。39714 条评论
Excel Skills for Business: Essentials

Excel Skills for Business: Essentials

Macquarie University
课程
评分为 4.9(满分 5 星)。25679 条评论
Python Data Structures

Python Data Structures

University of Michigan
课程
评分为 4.9(满分 5 星)。74505 条评论
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

deeplearning.ai
课程
评分为 4.7(满分 5 星)。11967 条评论
Tools for Data Science

Tools for Data Science

IBM
课程
评分为 4.5(满分 5 星)。19330 条评论
Python for Data Science and AI

Python for Data Science and AI

IBM
课程
评分为 4.6(满分 5 星)。18608 条评论
Marketing Analytics

Marketing Analytics

University of Virginia
课程
评分为 4.6(满分 5 星)。4906 条评论
Introduction to Data Science in Python

Introduction to Data Science in Python

University of Michigan
课程
评分为 4.5(满分 5 星)。21909 条评论
客户分析

客户分析

University of Pennsylvania
课程
Fundamentals of Quantitative Modeling

Fundamentals of Quantitative Modeling

University of Pennsylvania
课程
评分为 4.6(满分 5 星)。6480 条评论
Structuring Machine Learning Projects

Structuring Machine Learning Projects

deeplearning.ai
课程
评分为 4.8(满分 5 星)。43456 条评论
SQL for Data Science

SQL for Data Science

University of California, Davis
课程
评分为 4.6(满分 5 星)。6983 条评论
数据科学家的工具箱

数据科学家的工具箱

Johns Hopkins University
课程
评分为 4.6(满分 5 星)。28688 条评论
Forensic Accounting and Fraud Examination

Forensic Accounting and Fraud Examination

West Virginia University
课程
评分为 4.7(满分 5 星)。2807 条评论
数据驱动型公司的业务指标

数据驱动型公司的业务指标

Duke University
课程
评分为 4.6(满分 5 星)。7184 条评论
Data Science Math Skills

Data Science Math Skills

Duke University
课程
评分为 4.5(满分 5 星)。7706 条评论

    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.