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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-wrangling-with-python/
课程评论：没有评论
课程名称：使用Python进行数据整理 课程概述：数据要有用且具有意义，必须经过策划和精炼。本课程《使用Python进行数据整理》教授您这些过程的核心概念，并使您掌握该领域中最受欢迎的工具和技术。课程从Python的基础知识开始，主要关注数据结构。接着，课程深入探讨数据整理的基本工具，如NumPy和Pandas库。您将了解到为什么应避免使用其他语言中的传统数据清理方法，而应利用Python中专门的预构建例程。这种Python技巧和窍门的结合将展示如何使用相同的Python后端，从多个来源（包括互联网、大型数据库和Excel财务表）提取和转换数据。为了帮助您应对更具挑战性的场景，课程还将涵盖如何处理缺失或错误数据，并根据下游分析工具的要求重新格式化数据。课程通过实际案例和数据集帮助您掌握相关概念。完成本课程后，您将能够高效地使用多种来源来提取、清理、转换和格式化数据。 关于讲师： - Samik Sen：当前在机器学习领域使用R，拥有理论物理博士学位，曾为高性能计算研究生授课，并在国际会议上担任讲师，具有使用Perl处理数据和以gnuplot生成可视化图表的经验。 - Dr. Tirthajyoti Sarkar：在半导体技术领域担任高级首席工程师，应用前沿的数据科学和机器学习技术进行设计自动化和预测分析，定期撰写Python编程和数据科学相关主题的文章，拥有伊利诺伊大学的博士学位，以及斯坦福大学和麻省理工学院的人工智能和机器学习认证。 - Shubhadeep Roychowdhury：在一家总部位于巴黎的网络安全初创公司担任高级软件工程师，应用先进的计算机视觉和数据工程算法和工具开发尖端产品，常撰写关于Python算法实现等主题的文章，拥有西孟加拉技术大学的计算机科学硕士学位，并获得斯坦福大学的机器学习认证。
For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.About the AuthorSamik Sen is currently working with R on Machine Learning. He has done his Ph.D. in Theoretical Physics. He has Tutored Classes for High-Performance Computing postgraduates and Lecturer at International Conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in Artificial Intelligence and Machine learning from Stanford and MIT.Shubhadeep Roychowdhury works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying the state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics. He holds a master's degree in computer science from West Bengal University Of Technology and certifications in machine learning from Stanford.
