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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-for-professionals/
课程评论：没有评论
课程名称：职业数据科学 课程概述： 职业数据科学是获取深入且实用的数据科学技能的最佳途径。通过理论与实践相结合的方式，参与者将全面掌握如何在R语言（全球最受欢迎的数据科学语言）中管理、处理和可视化数据。 适合人群： 本课程面向那些厌倦使用电子表格进行分析的专业人士，适合希望通过代码提升工作质量和效率的人员。课程结束时，学员将建立坚实的R语言基础，并对现代数据科学领域有更深刻的理解。学员将学会如何使用R更好地分析数据，管理项目，创建互动演示，并与其他团队合作。任何涉及数据分析、报告或展示的人都将从学习数据科学编程中受益。 不适合人群： 本课程虽然在后期讨论机器学习的实例，但它并不专注于机器学习或统计学。尽管会介绍如何通过代码部署和评估不同类型的模型，包括机器学习算法，但课程以编程为出发点，而不是统计学。因此，相关的数学内容在本课程的范围之外，若需深入学习，需寻找特定的机器学习课程。 课程特点： - 采用大规模的现实案例引导学习过程，使数据科学的各个方面更直观易记。 - 处理不完美数据带来的各种挑战和bug，真实反映数据科学中最困难和耗时的部分。 - 关注学员的长期成功，提供带有说明和指导的可下载课程代码，以便于从学习到应用的顺利过渡。 通过本课程，您将朝着成为数据科学专业人士迈出重要的一步。
What is it?Data Science for Professionals is simply the best way to gain a in-depth and practical skill set in data science. Through a combination of theory and hands-on practice, course participants will gain a solid grasp of how to manage, manipulate, and visualize data in R - the world's most popular data science language.Who should take this course?This course is for professionals who are tired of using spreadsheets for analysis and have a serious interest in learning how to use code to improve the quality and efficiency of their work. At the end of this course, participants will have a developed a solid foundation of the fundamentals of the R language. Participants will have also gained a perspective on the modern data science landscape and how they can use R not only to better analyze data, but also to better manage projects, create interactive presentations, and collaborate with other teams. Whether it's spreadsheets, text documents, or slides, anyone who analyzes, reports, or presents data will benefit from a knowledge of data science programming.Who should NOT take this course?While this course covers examples of machine learning in later lectures, this is not a machine learning or a statistics-focused course. The course does go through examples of how to use code to deploy and assess different types of models, including machine learning algorithms, but it does so from a coding perspective and not a statistics perspective. The reason is that the math behind most machine learning algorithms merits a course entirely on its own. There are many courses out there that make dubious claims of easy mastery of machine learning and deep learning algorithms - this is not one of those courses.A Different kind of data science courseThis course is different from most other courses in several ways:We use very large, real-world examples to guide our learning process. This allows us to tie-together the various aspects of data science in a more intuitive, easy-to-retain manner.We encounter and deal-with various challenges and bugs that arise from imperfect data. Most courses use ideal datasets in their examples, but these are not common in the real-world, and solving data-related issues is usually the most difficult and time-consuming part of data science.We are focused on your long-term success. Our downloadable course code is filled with notes and guidance aimed at making the transition from learning-to-applying as smooth as possible.
