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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What is Data Science?

What is Data Science?

IBM
課程
評分為 4.7(滿分 5 星)。 38185 條評論
Excel Skills for Business: Essentials

Excel Skills for Business: Essentials

Macquarie University
課程
評分為 4.9(滿分 5 星)。 22028 條評論
Python Data Structures

Python Data Structures

University of Michigan
課程
評分為 4.9(滿分 5 星)。 69720 條評論
Tools for Data Science

Tools for Data Science

IBM
課程
評分為 4.5(滿分 5 星)。 18804 條評論
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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

deeplearning.ai
課程
評分為 4.7(滿分 5 星)。 10947 條評論
Python for Data Science and AI

Python for Data Science and AI

IBM
課程
評分為 4.6(滿分 5 星)。 18046 條評論
Fundamentals of Quantitative Modeling

Fundamentals of Quantitative Modeling

University of Pennsylvania
課程
評分為 4.6(滿分 5 星)。 6313 條評論
Introduction to Data Science in Python

Introduction to Data Science in Python

University of Michigan
課程
評分為 4.5(滿分 5 星)。 20835 條評論
Structuring Machine Learning Projects

Structuring Machine Learning Projects

deeplearning.ai
課程
評分為 4.8(滿分 5 星)。 42305 條評論
客户分析

客户分析

University of Pennsylvania
課程
Marketing Analytics

Marketing Analytics

University of Virginia
課程
評分為 4.6(滿分 5 星)。 4516 條評論
Forensic Accounting and Fraud Examination

Forensic Accounting and Fraud Examination

West Virginia University
課程
評分為 4.7(滿分 5 星)。 2605 條評論
数据科学家的工具箱

数据科学家的工具箱

Johns Hopkins University
課程
評分為 4.6(滿分 5 星)。 27763 條評論
Data Science Methodology

Data Science Methodology

IBM
課程
評分為 4.6(滿分 5 星)。 14439 條評論
临床研究数据管理

临床研究数据管理

Vanderbilt University
課程
評分為 4.7(滿分 5 星)。 835 條評論
Data Analysis with Python

Data Analysis with Python

IBM
課程
評分為 4.7(滿分 5 星)。 11667 條評論

    關於 数据分析 的常見問題

  • 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.