데이터 분석

데이터 분석 강좌는 대규모 데이터세트를 관리하고 분석하는 방법을 다룹니다. 데이터 마이닝, 빅 데이터 애플리케이션, 데이터 제품 개발을 공부하여 데이터 과학자로서의 경력을 쌓으실 수 있습니다.

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Python Data Structures
University of Michigan
Python Data Structures
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Excel Skills for Business: Essentials
Macquarie University
Excel Skills for Business: Essentials
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Introduction to Data Science in Python
University of Michigan
Introduction to Data Science in Python
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What is Data Science?
IBM
What is Data Science?
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The Data Scientist’s Toolbox
Johns Hopkins University
The Data Scientist’s Toolbox
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Marketing Analytics
University of Virginia
Marketing Analytics
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Python for Data Science and AI
IBM
Python for Data Science and AI
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Data Science Math Skills
Duke University
Data Science Math Skills
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R Programming
Johns Hopkins University
R Programming
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Using Databases with Python
University of Michigan
Using Databases with Python
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Excel Skills for Business: Intermediate I
Macquarie University
Excel Skills for Business: Intermediate I
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Data Analysis with Python
IBM
Data Analysis with Python
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Structuring Machine Learning Projects
deeplearning.ai
Structuring Machine Learning Projects
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Tools for Data Science
IBM
Tools for Data Science
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Databases and SQL for Data Science
IBM
Databases and SQL for Data Science
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SQL for Data Science
University of California, Davis
SQL for Data Science
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Data Science Methodology
IBM
Data Science Methodology
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Cloud Computing Basics (Cloud 101)
LearnQuest
Cloud Computing Basics (Cloud 101)
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Introduction to Big Data
University of California San Diego
Introduction to Big Data
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    데이터 분석에 대한 자주 묻는 질문

  • 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.