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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-data-science/
课程评论:没有评论
**课程名称:** 数据科学导论 **课程概述:** 本课程旨在教授学员如何利用 R 编程语言来执行数据科学项目,培养成为一名合格的数据科学家。学员将学习如何运用机器学习和预测性分析来实施商业解决方案。R 语言是处理日常数据科学任务的有力工具,本课程将引导您将 R 编程语言与实用的统计技术应用于日常商业场景。通过本课程,您将掌握现代数据科学家赖以识别趋势和提出行动建议的可视化、统计模型和数据处理工具。 **学习目标:** * 理解数据科学,成为更有效的数据分析师。 * 熟练使用 R 和 RStudio。 * 掌握建模和机器学习技术。 * 学会加载、可视化和解读数据。 * 利用 R 分析数据并提出有价值的商业解决方案。 **课程内容:** 本课程将从一个数据科学项目模板开始,随后深入 R 统计编程语言。您将学习数据加载、变量处理、缺失值处理,以及如何解读和可视化数据。课程将涵盖建模和机器学习,教授您如何使用现有的机器学习算法在 R 中为企业创建模型,并进行模型的验证和评估。您将学会如何就数据得出可靠的结论,即使面对现实世界的挑战。 **先修要求:** 本课程适合具备分析思维、熟悉基础统计学和编程/脚本语言的学员。虽然有 R 基础会很有帮助,但学员也可以在学习过程中掌握 R。 **学习成果:** 完成本课程后,您将具备以下能力: * 更深入地理解应用预测建模方法。 * 熟练使用 R 中的现有机器学习方法。 * 能够与团队成员在数据科学项目中协作,发现问题并提出解决方案。 * 自信地分析各种来源的数据,并分享能提升企业竞争力及成功的结论。 **重要提示:** 本课程侧重于使用 R 中的现有机器学习方法,但不教授如何从头开始实现这些算法。学员应具备基础统计学和基础脚本/编程知识。
Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action. Understand Data Science to Be a More Effective Data Analyst ●Use R and RStudio ●Master Modeling and Machine Learning ●Load, Visualize, and Interpret Data Use R to Analyze Data and Come Up with Valuable Business Solutions This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go. You'll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems. All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders. Contents and Overview This course begins with a walk-through of a template data science project before diving into the R statistical programming language. You will be guided through modeling and machine learning. You'll use machine learning methods to create algorithms for a business, and you'll validate and evaluate models. You'll learn how to load data into R and learn how to interpret and visualize the data while dealing with variables and missing values. You'll be taught how to come to sound conclusions about your data, despite some real-world challenges. By the end of this course, you'll be a better data analyst because you'll have an understanding of applied predictive modeling methods, and you'll know how to use existing machine learning methods in R. This will allow you to work with team members in a data science project, find problems, and come up solutions. You'll complete this course with the confidence to correctly analyze data from a variety of sources, while sharing conclusions that will make a business more competitive and successful. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming.