Definitions

In general terms, Data Science is the extraction of knowledge from data, which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD). It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information theory and information technology, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, and high performance computing. Methods that scale to Big Data are of particular interest in data science, although the discipline is not generally considered to be restricted to such data. The development of machine learning, a branch of artificial intelligence used to uncover patterns in data from which predictive models can be developed, has enhanced the growth and importance of data science.

概括而言，数据科学是从数据中提取知识，它是数据挖掘和预测分析领域的延续，也称为知识发现和数据挖掘（KDD）。它采用数学、统计学、信息论和信息技术等广泛领域中的许多技术和理论，包括信号处理、概率模型、机器学习、统计学习、数据挖掘、数据库、数据工程、模式识别和学习、可视化、预测分析、不确定性建模、数据仓库、数据压缩、计算机编程和高性能计算。数据科学特别关注可扩展到大数据的方法，尽管该学科通常不被认为局限于此类数据。机器学习是人工智能的一个分支，用于发现数据中的模式，并从中开发预测模型；机器学习的发展增强了数据科学的发展和重要性。

Fields contributing to data science

• Data Engineering includes techniques that harness resources for building scalable data systems when the characteristics of the datasets require architectures for collection, storage, manipulation, and analysis.

  数据工程包括当数据集的特性需要收集、存储、操作和分析的架构时利用资源构建可扩展数据系统的技术。

• Data Analysis includes techniques that pre-process, statistically analyse, and then present results of analysis, for instance in visualisations. There are many variations of these field and specialisations of it exist in many application domains.

  数据分析包括预处理、统计分析以及最终呈现分析结果（例如以可视化形式呈现）的技术。该领域有很多变体，并且在许多应用领域中存在其专业化。

• Data Management includes techniques to manage data through its lifecycle and in accordance with regulations and best practices to address issues such as privacy, security, and appropriate access. Often the terms governance and curation are used too.

  数据管理包括在数据生命周期内管理数据的技术，并遵循法规和最佳实践来解决隐私、安全和适当访问等问题。通常也使用“治理”和“策展”等术语。

What is Data Science

A data scientist is a practitioner who has sufficient knowledge in the overlapping regimes of business needs, domain knowledge, analytical skills, and software and systems engineering to manage the end-to-end data processes through each stage in the data lifecycle. Moreover,

Data science is the empirical synthesis of actionable knowledge from raw data through the complete data lifecycle process.

数据科学家是一位实践者，在业务需求、领域知识、分析技能以及软件和系统工程等交叉领域拥有丰富的知识，能够管理数据生命周期各个阶段的端到端数据流程。此外， 数据科学是从原始数据到完整数据生命周期流程的可操作知识的实证综合。

A functional development

several related factors that emerged as drivers of Data Science from 2000 onwards

• Business pressure on decision making: businesses function in a continuously changing environment.

  • The more data an organization has, the more onerous the task of keeping order to the data.

    组织拥有的数据越多，维护数据秩序的任务就越繁重。

  • Businesses saw a need to convert to data driven decision making in line with earlier advances such as just-in-time manufacturing. Operations became too big for the intuitive leader with the right hunches to operate without analytic guidance.

    企业意识到，需要转型为数据驱动的决策模式，以顺应即时生产等早期技术的进步。运营规模变得过于庞大，以至于即使是拥有敏锐直觉和正确预感的领导者，也无法在缺乏分析指导的情况下开展运营。

  • Daily operation of businesses require them to reach insights faster and act on them in real time.

    企业的日常运营需要他们更快地获得洞察力并实时采取行动。

• Moore's Law changed the landscape

  according to Moore's Law, which held up until recently, computing power and storage capacity is growing exponentially (so doubling in constant time). Moreover, according to Bell's Law, different computing platforms and ecosystems will continue to emerge, the laptop, the mobile phone, cloud computing etc.

  根据摩尔定律（Moore’s Law），直到最近之前，这一定律一直有效：计算能力和存储容量正以指数级增长（即在固定时间内翻倍）。此外，根据贝尔定律（Bell’s Law），不同的计算平台和生态系统将持续不断地出现，例如笔记本电脑、手机、云计算等。

• Data itself is an asset

  "Data is the most valuable outcome of building a successful product. It's the insight, the secret, the keys to the kingdom. Don't sell the keys to the kingdom. Data provides economies of scale and insights used to develop huge barriers to entry and it should be kept within an organization. Internal data use is the path to building a huge business."

The hype cycle  炒作周期

Government sponsored scientific disciplines

As with the emergence of any major field, significant government sponsored efforts have begun.