Data Science数据分析科学

在过去的20年里,人们投资大量商业领域,也同时提高了产业的数据收集能力。现在商业的各个环节都配备数据采集功能,比如说运营,生产,供应链管理,顾客行为,市场营销,工作流程。数据广泛存在,我们要思考的不仅仅是如何采集,而是如何能从数据中提取出有用的信息和知识。我们最终的目标是利用数据分析科学帮助我们更好的拿主意。

The past 20 years have seen extensive investments in business infrastructure, which have in the meanwhile improved the ability to collect data throughout the industry. Virtually every aspect of business is equipped with data collection functionality: operations, manufaturing, supply-chain management, customer behavior, marketing campaign performance, workflow procedures. Despite the broad availability of data, we shall not only focus on how to collect data but also how to extract useful information and knowledge from data. Our ultimate goal is to improve decision making in the context of business using data science.

那么如何应用数据分析科学驱动商业决策呢?我们首先要理解一个机构的数据相关作业的背景。商业决策是建立在数据分析科学或者数据分析思维的基础之上,我们要区分数据分析科学与数据工程。数据分析科学需要使用数据,数据又可以通过数据工程提供。数据处理技术辅助数据工程,但却可以应用到更多的任务中,比如高效交易处理,现代网络系统流程作业,以及网上广告营销的管理。大数据技术其实也是一种数据处理技术,用于处理传统数据技术无法处理的大数据,当然也可以用于支持数据挖掘任务以及其他数据分析科学活动。

So how do we apply Data Science to drive decision making in practise? We first of all need to understand the various data-related processes in the organization. Business decision-making is based on the data science or data analytical thinking. We need to distinguish it from data engineering. Data science needs access to data, which are provided by data engineering. The data processing technologies facilitate data engineering and are useful for more, such as efficient transaction process, modern web system processing and online advertisement. “Big data” technologies are used for processing the large datasets that traditional processing systems couldn’t deal with, and also could be used for data processing in support of the data mining techniques and other data science activities.

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