|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-python-effective-data-analysis-using-python/
课程评论：没有评论
课程名称：学习路径：Python：有效的数据分析 课程概述：随着时间的推移，几乎每个组织都认识到了数据分析的重要性。可以毫不夸张地说，“如果一个组织不分析数据，就无法在当今竞争激烈的环境中生存。” 数据分析是一个将源数据提炼以获取有用信息并做出有效预测的过程。在本学习路径中，我们将学习如何使用Python强大的工具集进行数据分析。Packt的学习视频路径是一系列逻辑清晰、循序渐进的个别视频产品，每个视频都建立在前一个视频所学技能的基础上。 Python拥有众多数值和数学工具包，如Numpy、Scipy、Scikit-learn和SciKit，这些都用于数据分析和机器学习。因此，Python成为数据科学家进行数据分析、可视化和机器学习的首选语言。我们将对数据分析进行总体回顾，并详细讨论网络抓取工具和技术。我们将展示一系列丰富的示例，旨在解决使用Python抓取网站时常见和非常规的问题，深入探讨Python网络抓取工具的能力，如Selenium、BeautifulSoup和urllib2。 此外，我们还将讨论可视化的最佳实践。有效的可视化能够帮助更好地从数据中获取洞察，并帮助做出更好的业务决策。完成本学习路径后，您将能够使用Python网络抓取工具从动态和复杂的网站中提取数据，并更好地理解数据可视化概念。您还将学习如何应用这些概念，并在实施过程中克服各种挑战。 为了确保您获得最佳的学习体验，本学习路径结合了多位业界领先作者的作品。课程讲师包括： - Benjamin Hoff：曾作为软件工程师和团队负责人，从事图形处理和科学设施模拟，探索自然语言处理和机器学习的前沿项目。 - Charles Clayton：独立网络开发者，专注于Python网络抓取解决方案，经验丰富。 - Dimitry Foures：拥有应用数学和理论物理背景的数据科学家，目前在剑桥一家智能能源初创公司工作。 - Giuseppe Vettigli：在研究和学术界工作多年的数据科学家，专注于机器学习模型和应用开发。 - Igor Milovanović：经验丰富的软件开发者，具备Linux系统知识和软件工程教育背景。 本课程将为您提供实用的技能，以应对数据分析和可视化中的各种挑战。
Over the years, almost every organization has understood the importance of analyzing data. In fact, it would not be an overstatement to say that "No organization will be able to survive today's cut-throat competition if it does not analyze data." Data analysis as we know it is the process of taking the source data, refining it to get useful information, and then making useful predictions from it. In this Learning Path, we will learn how to analyze data using the powerful toolset provided by Python. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Python features numerous numerical and mathematical toolkits such as Numpy, Scipy, Scikit learn, and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice of data scientists for data analysis, visualization, and machine learning. We will have a general look at data analysis and then discuss the web scraping tools and techniques in detail. We will show a rich collection of recipes that will come in handy when you are scraping a website using Python, addressing your usual and unusual problems while scraping websites by diving deep into the capabilities of Python's web scraping tools such as Selenium, BeautifulSoup, and urllib2. We will then discuss the visualization best practices. Effective visualization helps you get better insights from your data, and help you make better and more informed business decisions. After completing this Learning Path, you will be well-equipped to extract data even from dynamic and complex websites by using Python web scraping tools, and get a better understanding of the data visualization concepts. You will also learn how to apply these concepts and overcome any challenge while implementing them. To ensure that you get the best of the learning experience, in this Learning Path we combine the works of some of the leading authors in the business. About the authors Benjamin Hoff spent 3 years working as a software engineer and team leader doing graphics processing, desktop application development, and scientific facility simulation using a mixture of C++ and Python. This sparked a passion for software development and developmental programming and led him to explore state-of-the art projects in natural language processing, facial detection/recognition, and machine learning. Charles Clayton is a sole proprietor of crclayton technologies co, and an independent web developer. He is an experienced developer and Python specialist in Python web scraping solutions and tools such as Selenium, BeautifulSoup, and urllib2. He also has worked as a Reliability Engineer with West frazweer. Dimitry Foures is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at École Polytechnique in Paris where he obtained first class in Master's degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data scientist for a smart energy startup in Cambridge, in close collaboration with the university. Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualization in Python in his blogs. Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems.
