Data Science数据分析科学


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.


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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