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所在平台: Udemy |
课程主页: https://www.udemy.com/course/r-for-data-science-first-step-data-scientist/
课程评论：没有评论
**课程名称：** 数据科学训练营：迈出数据科学家第一步 **课程概述：** 本课程旨在为初学者提供一个全面入门数据科学的平台，重点关注理论与实操相结合。课程将深入讲解线性回归和逻辑回归的原理，揭示树基模型的工作机制，并教授如何评估预测模型。学员将有机会完成一个完整的 Kaggle 项目，从而将所学知识付诸实践。 **课程特色：** * **代码实践：** 提供大量“边学边练”的讲座，演示如何在 R 语言中实现所学算法。 * **知识巩固：** 通过不同难度的测试题和实践练习，帮助学员检验和巩固知识。 * **R 语言实战：** 侧重于 R 语言的实际编程应用，深入剖析模型训练过程，而非仅仅学习自动构建算法的函数。R 作为数据科学领域广泛使用的语言，具备现代、灵活的特点，学习曲线平缓，能帮助专业人士快速构建预测模型。 * **数据处理：** 学习使用 Dplyr 包进行数据操作，因为数据准备在数据科学项目中占据重要比例。 **学习收获：** 完成本课程后，学员将能够： * 解决回归问题，例如使用线性回归或回归树。 * 解决分类问题，如使用逻辑回归或分类树。 * 掌握使用不同指标评估算法的方法。 * 理解偏差与方差的概念。 * 运用随机森林算法并理解其原理。 * 熟练使用 Dplyr 进行数据处理。 * 独立完成一个 Kaggle 数据科学项目。 本课程鼓励学员加入数据科学的学习行列，探索 R 语言在统计分析领域的强大功能，并会根据学员反馈持续更新。
So are you looking to jump into one of the most exciting fields to work on today? And are you looking for a course that explains all the theory behind algorithms with coding?This course was designed to be your first complete step into Data Science! We will delve deeper into the concepts of Linear and Logistic Regression, understand how Tree Based models work and learn how to evaluate predictive models. Additionally, you will develop your first end-to-end kaggle project!This course contains lectures around the following groups: Code along lectures where you will see how we can implement the stuff we will learn;Test your knowledge with questions and practical exercises with different levels of difficulty;This course was designed to be focused on the practical side of coding in R - other than studying the functions that let us build algorithms automatically we will investigate deeply how models are trained and how they get to the optimum solution to solve our data science challenges. And why will we use R? R is one of the de facto languages for a lot of Data Science projects today - either for enterprise-level projects or research, R is a modern and flexible language with a smooth learning curve that enables most professionals to build predictive models in quick fashion.At the end of the course you should be able to contribute to data science projects - understanding the choices you have to make when it comes to algorithms and learn how to evaluate those choices. Along the way you will also learn how to manipulate data with Dplyr because a huge percentage of the time spent in a Data Science project is focused on data preparation!Here are some examples of things you will be able to do after finishing the course:Solving Regression problems using Linear Regression or Regression Trees.Solving Classification problems using Logistic Regression or Classification Trees.Learn how to evaluate algorithms using different metrics.Understanding the concept of bias and variance.Using Random Forests and understanding the reasoning behind them.Manipulating data using Dplyr.Build your own Kaggle Data Science project!Join thousands of professionals and students in this Data Science journey and discover the amazing power of R as a statistical open-source language. This course will be constantly updated based on students feedback.
