Udemy: R Programming

数据科学是一门令人兴奋的学科，它允许您将原始数据转化为理解、洞察力和知识。“R for Data Science”的目标是帮助您学习 R 中最重要的工具，这些工具将使您能够进行数据科学。完成本课程后，您将拥有使用 R 的最佳部分来应对各种数据科学挑战的工具。

你会学到什么

数据科学是一个巨大的领域，你不可能通过一门课程就掌握它。本课程的目标是为您提供最重要工具的坚实基础

首先，您必须将数据导入 R。这通常意味着您将存储在文件、数据库或 Web API 中的数据加载到 R 中的数据框中。如果您无法将数据导入 R，则您不能对它进行数据科学！

导入数据后，最好对其进行整理。整理数据意味着以一致的形式存储数据，使数据集的语义与其存储方式相匹配。简而言之，当您的数据整洁时，每一列都是一个变量，每一行都是一个观察值。整洁的数据很重要，因为一致的结构使您可以将精力集中在有关数据的问题上，而不是努力将数据转换为适合不同功能的正确形式。

一旦您拥有整洁的数据，常见的第一步就是对其进行转换。转换包括缩小感兴趣的观察范围（例如一个城市的所有人，或去年的所有数据），创建作为现有变量的函数的新变量（例如根据距离和时间计算速度），以及计算一组汇总统计数据（如计数或均值）。整理和转换一起称为争吵，因为以一种自然处理的形式获取数据通常感觉就像一场战斗！

一旦您拥有包含所需变量的整洁数据，知识生成的主要引擎有两个：可视化和建模。它们具有互补的优势和劣势，因此任何真正的分析都会在它们之间迭代多次。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 12 lectures (6 hour, 40 mins) | Size: 3.02 GB
R for Data Science

What you’ll learn

You will learn the most important tools in R that will allow you to do data science
You will learn to install R studio
You will have the tools to tackle a wide variety of data science challenges, using the best parts of R.
You will learn to import your data into R
You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.
You will learn Visualisation, it is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data
You will learn Models, they are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well.

Requirements

You should be generally numerically literate, and it’s helpful if you have some programming experience already.

Description

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science. After going through this course, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.

What you will learn

Data science is a huge field, and there’s no way you can master it by going through a single course. The goal of this course is to give you a solid foundation in the most important tools

First, you must import your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can’t get your data into R, you can’t do data science on it!

Once you’ve imported your data, it is a good idea to tidy it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.

Once you have tidy data, a common first step is to transform it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called wrangling, because getting your data in a form that’s natural to work with often feels like a fight!

Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualization and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.

Prerequisites:

You should be generally numerically literate, and it’s helpful if you have some programming experience already.
Who this course is for:

Beginner R programmers curious about data science