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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-fundamentals-m/
课程评论：没有评论
课程名称：数据科学基础 概述：在本课程中，我们将探索和理解数据科学的概念与细节。数据科学是一个跨学科的领域，利用科学方法、过程、算法和系统，从嘈杂、结构化和非结构化数据中提取知识和洞见，并将这些知识和可操作的见解应用于广泛的领域。数据科学与数据挖掘、机器学习和大数据密切相关。它将统计学、数据分析、信息学及其相关方法统一为一个概念，以通过数据“理解和分析实际现象”。数据科学采用来自数学、统计学、计算机科学、信息科学和领域知识等多个领域的技术和理论，但与计算机科学和信息科学有所不同。 图灵奖得主吉姆·格雷曾将数据科学想象为科学的“第四范式”（经验、理论、计算和现在的数据驱动），并断言“信息技术的影响正在改变科学的一切”，以及数据洪流的出现。课程还将对大数据的概念和细节进行探讨。数据科学家是指那些结合编程代码与统计知识以从数据中创造洞见的人。 大数据处理是一个关注如何分析、系统提取信息或处理数据集（过于庞大或复杂，以至于传统数据处理应用软件无法应对）的方法。具有更多字段（列）的数据提供了更强的统计能力，而复杂性更高的数据（具有更多属性或列）可能导致更高的假发现率。大数据分析面临的挑战包括数据捕获、存储、分析、搜索、共享、传输、可视化、查询、更新、信息隐私和数据源等。大数据最初与三个关键概念相关：量、种类和速度。大数据分析的挑战在于采样，因此以前只能依赖观察和采样。 当前“大数据”一词通常指的是利用预测分析、用户行为分析或某些其他高级数据分析方法从大数据中提取价值，而不再特别指代数据集的大小。虽然可以肯定现在可用的数据量确实庞大，但这不是对新数据生态系统最相关的特点。数据集的分析可以发现新的关联，从而“识别商业趋势、预防疾病、打击犯罪”等。科学家、商业高管、医疗从业者、广告商和政府在互联网搜索、金融科技、医疗分析、地理信息系统、城市信息学和商业信息学等领域，常常面临大数据集的困难。科学家在电子科学工作中也会遇到，包括气象学、基因组学、连接组学、复杂物理模拟、生物学和环境研究等领域的限制。
On this training we are going to explore and understand the concepts and details of Data Science. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.We also are going to take a peek at the Big Data Concepts and details of Big Data. A data scientist is someone who creates programming code, and combines it with statistical knowledge to create insights from data.Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on". Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research.
