Managing Data Analysis
link Источник: www.coursera.org
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assignment Уровень : Начальный
chat_bubble_outline Язык : английский
language Субтитры : японский
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4.4
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timer 4 час(ы) курса

Резюме

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD

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Программа

  • Week 1 - Managing Data Analysis
    Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the l...
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Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

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Разработчик

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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Платформа

Coursera

Coursera - это цифровая компания, предлагающая массовые открытые онлайн-курсы, основанные учителями компьютеров Эндрю Нгом и Стэнфордским университетом Дафни Коллер, расположенные в Маунтин-Вью, штат Калифорния.

Coursera работает с ведущими университетами и организациями, чтобы сделать некоторые из своих курсов доступными в Интернете, и предлагает курсы по многим предметам, включая: физику, инженерию, гуманитарные науки, медицину, биологию, социальные науки, математику, бизнес, информатику, цифровой маркетинг, науку о данных и другие предметы.

Оценка сообщества
4.4 /5 Средняя
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Контент
4.4/5
Платформа
4.4/5
Анимация
4.4/5
Лучшая оценка

very good overview of data analysis for beginners (but cannot hurt any kind of audience, it never hurts to have some guidance, process ;)Really good pace and concrete examples / use cases to illustrate every classes.

Аноним
19 февраля 2018 г.
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Содержание
5/5
Платформа
5/5
Анимация
5/5
19 февраля 2018 г.
starstarstarstarstar

very good overview of data analysis for beginners (but cannot hurt any kind of audience, it never hurts to have some guidance, process ;)Really good pace and concrete examples / use cases to illustrate every classes.

1 февраля 2018 г.
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It would be nice to have listed points or tables to summarise/compare context. As well, it would be good to introduce example separately rather than mixing with the explanation of subject.

12 января 2018 г.
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This felt a lot more detailed that the previous courses (which was great!) and I feel like I've genuinely learned some stuff (the six type of questions) that I can use!

21 декабря 2017 г.
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Very valuable, however, in particular the section on inference vs prediction included material not explained before and hard to follow. Also examples with t-values and interpretation of values when adding confounders was difficult to grasp.

12 ноября 2017 г.
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The topics in the course shows that there is a set of steps to counduct a data science project since the definition of the question to solve to the apropiate way to communicate the results. The content of some videos could be considered technical.