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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-with-r/
课程评论：没有评论
**课程名称：** R语言数据科学（从入门到精通） **课程概述：** Uplatz的“R语言数据科学”课程是一门为初学者到进阶者设计的全面课程。数据科学是一个融合了数学、商业洞察、工具、流程和机器学习技术的综合性领域。通过这些技术的结合，我们可以从原始数据中发现有价值的洞察和模式，从而为重要的商业决策提供支持。作为一名数据科学家，您的职责是识别需要回答的问题，寻找相关数据，并具备商业洞察、分析服务能力，以及挖掘、清洗和呈现数据的技能。企业依赖数据科学家来获取、管理和分析大量的非结构化数据。 R语言是一种强大且广泛用于数据分析和统计计算的编程语言，开发于20世纪90年代初。作为一种开源软件，R具有灵活性和开放性，允许与其他应用程序和系统集成，并且由于其开源特性，其质量也得到了广泛的认可和迭代。R语言提供了丰富的对象、运算符和函数，帮助用户发现、建模和可视化数据。 在商业领域，R语言数据科学拥有广阔的前景。作为分析领域中最常用的开源语言，无论是小型还是大型项目，各公司都倾向于选择R语言。因此，掌握R语言数据科学的专业人才需求量持续增长。 Uplatz提供的这门综合课程将深入讲解数据科学的概念，并演示如何利用R语言进行实现和应用。 **课程大纲：** 1. **数据科学导论** * 数据科学流程 * 数据科学项目阶段 * 设定预期 * 总结 2. **将数据加载到R中** * 处理文件数据 * 处理关系型数据库 * 总结 3. **数据管理** * 数据清洗 * 模型和验证采样 * 总结 4. **模型选择与评估** * 将问题映射到机器学习任务 * 模型评估 * 模型验证 * 总结 5. **记忆方法** * 决策树应用 * 总结 6. **线性与逻辑回归** * 线性回归应用 * 逻辑回归应用 * 总结 7. **无监督学习方法** * 聚类分析 * 关联规则 * 总结 8. **探索高级方法** * 使用Bagging和随机森林减少训练方差 * 使用广义可加模型(GAMs)学习非单调关系 * 使用核方法增加数据分离度 * 使用SVMs建模复杂决策边界 9. **文档与部署** * "buzz"数据集 * 使用Knitr生成里程碑文档
A warm welcome to the Data Science with R course by Uplatz.Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques. A mix of all these fields help us in discovering the visions or designs from raw data which can be of major use in the formation of big business decisions. As a Data scientist it's your role to inspect which questions want answering and where to find the related data. A data scientist should have business insight and analytical services. One also needs to have the skill to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.R is a commanding language used extensively for data analysis and statistical calculating. It was developed in early 90s. R is an open-source software. R is unrestricted and flexible because it's an open-source software. R's open lines permit it to incorporate with other applications and systems. Open-source soft wares have a high standard of quality, since multiple people use and iterate on them. As a programming language, R delivers objects, operators and functions that allow employers to discover, model and envision data. Data science with R has got a lot of possibilities in the commercial world. Open R is the most widely used open-source language in analytics. From minor to big initiatives, every other company is preferring R over the other languages. There is a constant need for professionals with having knowledge in data science using R programming.Uplatz provides this comprehensive course on Data Science with R covering data science concepts implementation and application using R programming language.Data Science with R - Course Syllabus1. Introduction to Data Science1.1 The data science process1.2 Stages of a data science project1.3 Setting expectations1.4 Summary2. Loading Data into R2.1 Working with data from files2.2 Working with relational databases2.3 Summary3. Managing Data3.1 Cleaning data3.2 Sampling for modeling and validation3.3 Summary4. Choosing and Evaluating Models4.1 Mapping problems to machine learning tasks4.2 Evaluating models4.3 Validating models4.4 Summary5. Memorization Methods5.1 Using decision trees 1275.2 Summary6. Linear and Logistic Regression6.1 Using linear regression6.2 Using logistic regression6.3 Summary7. Unsupervised Methods7.1 Cluster analysis7.2 Association rules7.3 Summary8. Exploring Advanced Methods8.1 Using bagging and random forests to reduce training variance8.2 Using generalized additive models (GAMs) to learn nonmonotone relationships8.3 Using kernel methods to increase data separation8.4 Using SVMs to model complicated decision boundaries9. Documentation and Deployment9.1 The buzz dataset9.2 Using knitr to produce milestone documentation
