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所在平台: Udemy |
课程主页: https://www.udemy.com/course/programming-python-and-r-with-data-science/
课程评论：没有评论
本课程“Python & R 编程”旨在教授数据科学中两种最流行的编程语言：Python 和 R。 **R 语言**： * 由 Ross Ihaka 和 Robert Gentleman 于 1995 年创建，作为 S 编程语言的实现，专注于数据分析、统计和图形模型。 * 尤其擅长数据可视化。 * 通常用于独立计算或在单个服务器上进行分析，适合探索性工作。 * 拥有大量现成的包，如 dplyr、plyr、data.table（数据处理）、stringr（字符串操作）、zoo（时间序列）、ggvis、lattice、ggplot2（数据可视化）和 caret（机器学习）。 * 推荐使用 RStudio IDE。 **Python 语言**： * 由 Guido Van Rossum 于 1991 年创建，强调生产力和代码可读性。 * 适用于需要与 Web 应用集成或将统计代码嵌入生产数据库的任务。 * 作为一门成熟的编程语言，非常适合实现生产用途的算法。 * 关键数据分析包包括 NumPy/SciPy（科学计算）、pandas（数据处理）、matplotlib（图形绘制）和 scikit-learn（机器学习）。 * 推荐的 IDE 包括 Spyder、IPython Notebook 和 Rodeo。 **课程建议**： * 作为数据科学家，需要根据具体问题、学习成本、领域常用工具以及其他可用工具的关系来选择合适的语言。 * 强烈建议学习这两种语言，并在工作中根据需要灵活使用，因为许多数据科学团队同时使用 R 和 Python。 **学生反馈**： 学生普遍对课程表示满意，认为课程内容充实，有助于提升专业技能，并期待将所学应用于实际问题。
Both Python and R are popular programming languages for Data Science. While R's functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax.Ross Ihaka and Robert Gentleman created the open-source language R in 1995 as an implementation of the S programming language. The purpose was to develop a language that focused on delivering a better and more user-friendly way to do data analysis, statistics and graphical models.Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.As a data scientist it's your job to pick the language that best fits the needs. Some questions that can help you:What problems do you want to solve?What are the net costs for learning a language?What are the commonly used tools in your field?What are the other available tools and how do these relate to the commonly used tools?When and how to use R?R is mainly used when the data analysis task requires standalone computing or analysis on individual servers. It's great for exploratory work, and it's handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly. R can even be part of a big data solution.When getting started with R, a good first step is to install the amazing RStudio IDE. Once this is done, we recommend you to have a look at the following popular packages:dplyr, plyr and data.table to easily manipulate packages,stringr to manipulate strings,zoo to work with regular and irregular time series,ggvis, lattice, and ggplot2 to visualize data, andcaret for machine learningWhen and how to use Python?You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a fully fledged programming language, it's a great tool to implement algorithms for production use.While the infancy of Python packages for data analysis was an issue in the past, this has improved significantly over the years. Make sure to install NumPy /SciPy (scientific computing) and pandas (data manipulation) to make Python usable for data analysis. Also have a look at matplotlib to make graphics, and scikit-learn for machine learning.Unlike R, Python has no clear "winning" IDE. We recommend you to have a look at Spyder, IPython Notebook and Rodeo to see which one best fits your needs.* We recommend all our students to learn both the programming languages and use them where appropriate since many Data Science teams today are bilingual, leveraging both R and Python in their work.Testimonials:Yes, I am happy in taking this course. I have a confident that I will be doing better in coming days by learning this course. ~ Ch HemalathaYes a good match ~ Fashagba Tosin Sarahgood for basics and non technicals ~ Muhammad AzamIt is very good to me so far and I am looking forward to learning how data science impact our daily life and also how it can be applied to solve social problems. ~ Jacquline Andrew KayuniIt was quite good and challenging. ~ Ilesanmi Ayo Jimba
