Data Science Methodology

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/data-science-methodology

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课程详情

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think! LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

数据科学方法:尽管最近几十年来计算能力和对数据的访问最近有所增加,但我们在决策过程中使用数据的能力要么丢失,要么根本没有最大化,我们没有对提出的问题有深刻的理解,以及如何将数据正确地应用于当前的问题。 本课程有一个目的,就是分享一种可以在数据科学中使用的方法,以确保用于解决问题的数据是相关的并且可以正确处理以解决当前的问题。 因此,在本课程中,您将学习:     -解决数据科学问题的主要步骤。     -实践数据科学的主要步骤,从形成具体的业务或研究问题,到收集和分析数据,建立模型,以及理解模型部署后的反馈。     -数据科学家的想法! 限时优惠:订阅仅需每月39美元,即可访问分级材料和证书。

课程大纲

In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart.You will learn about the first two stages of the data science methodology, namely Data Requirements and Data Understanding. Finally, through a lab session, you will learn how to complete the Business Understanding and the Analytic Approach stages as well Data Requirements and Data Collection stages pertaining to any data science problem.

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Despite the recent increase in computing power and access to data over the last couple of decades, o

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