Examples of using Data science projects in English and their translations into Chinese
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Finding a good machine learning dataset is often the biggest hurdle a developer has to cross before starting any data science project.
Tools such as TFS or GIT should be part of the daily process of a Data Science project.
AI工程师必须与数据科学家，诸如TFS或GIT等工具应该成为数据科学项目日常流程的一部分。Reasons why most data science projects fail.
数据科学项目失败最常见的4个原因.Why most data science projects fail?
为何多数的大数据项目以失败告终？?Data science projects may impact business leaders across the company.
数据科学项目可能会影响整个公司的商业领袖。This turns out to be frequently useful for data science projects.
我经常发现它在我的大多数数据科学项目中很有用。Why do 87% of data science projects never make it into production?
为什么87％的数据科学项目从未投入生产？?A future post will dig deeper into writing tests for data science projects.
以后我会写一篇文章，深入讨论如何为数据科学项目写测试。Why do 87% of data science projects never make it into production?
原因之一是87%的数据科学项目从未投入生产。?Calculate return on investment, and go for the simpler data science projects first!
计算投资回报，先做简单的数据科学项目!Level 2: Data science projects take on greater complexity and leverage larger data volumes.
第2级：数据科学项目具有更高的复杂性并可以利用更大的数据量。What common mistakes beginners do when working on machine learning or data science projects?
初学者在从事机器学习或数据科学项目时经?What common mistakes beginners do when working on machine learning or data science projects?
初学者在从事机器学习或数据科学项目时经常会遇到一些常见的错误?CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects.
CRISP-DM是管理数据挖掘，预测分析和数据科学项目的领先方法。To showcase these skills, here are 5 types of data science projects for your portfolio.
为了展示这些技能，以下是你应该着重补充的5种数据科学项目组合类型：.A recent article states that 87% of all data science projects are never put into production.
上一篇:为什么87％的数据科学项目从未投入生产？A recent article states that 87% of all data science projects are never put into production.
原因之一是87%的数据科学项目从未投入生产。Where possible, do showcase any codes written, especially if the codes are written for data science projects.
在可能的情况下，应展示所写的代码，特别是如果代码是为数据科学项目编写的。In practice, this means you will be able to lead and manage your own data science projects.
在生产方面，你可以部署和管理数据科学项目。These posts discuss the different phases of data science projects and how are they different from regular software projects. .
这些文章讨论了数据科学项目的不同阶段，以及它们与常规软件项目有何不同。
