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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필터링 기준:
382 결과
정렬 기준:
What is Data Science?

What is Data Science?

IBM
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Excel Skills for Business: Essentials

Excel Skills for Business: Essentials

Macquarie University
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Python Data Structures

Python Data Structures

University of Michigan
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5점 만점에 4.9점을 받았습니다. 69340 리뷰
Tools for Data Science

Tools for Data Science

IBM
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5점 만점에 4.5점을 받았습니다. 18768 리뷰
Python for Data Science and AI

Python for Data Science and AI

IBM
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5점 만점에 4.6점을 받았습니다. 18003 리뷰
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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

deeplearning.ai
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5점 만점에 4.7점을 받았습니다. 10902 리뷰
Introduction to Data Science in Python

Introduction to Data Science in Python

University of Michigan
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5점 만점에 4.5점을 받았습니다. 20769 리뷰
Fundamentals of Quantitative Modeling

Fundamentals of Quantitative Modeling

University of Pennsylvania
강좌
5점 만점에 4.6점을 받았습니다. 6301 리뷰
Structuring Machine Learning Projects

Structuring Machine Learning Projects

deeplearning.ai
강좌
5점 만점에 4.8점을 받았습니다. 42240 리뷰
Marketing Analytics

Marketing Analytics

University of Virginia
강좌
5점 만점에 4.6점을 받았습니다. 4485 리뷰
Customer Analytics

Customer Analytics

University of Pennsylvania
강좌
Forensic Accounting and Fraud Examination

Forensic Accounting and Fraud Examination

West Virginia University
강좌
5점 만점에 4.7점을 받았습니다. 2589 리뷰
The Data Scientist’s Toolbox

The Data Scientist’s Toolbox

Johns Hopkins University
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5점 만점에 4.6점을 받았습니다. 27695 리뷰
Data Science Methodology

Data Science Methodology

IBM
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5점 만점에 4.6점을 받았습니다. 14426 리뷰
Data Management for Clinical Research

Data Management for Clinical Research

Vanderbilt University
강좌
5점 만점에 4.7점을 받았습니다. 833 리뷰
Data Analysis with Python

Data Analysis with Python

IBM
강좌
5점 만점에 4.7점을 받았습니다. 11641 리뷰

    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.