Data Science Specialization

开始时间: 06/21/2022 持续时间: Approximately 11 months to complete Suggested pace of 7 hours/week

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew

课程主页: https://www.coursera.org/specializations/jhu-data-science

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课程详情

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

数据科学专业化:提出正确的问题,处理数据集并创建可视化效果以传达结果。 本专业涵盖了整个数据科学管道中所需的概念和工具,从提出正确的问题到做出推理和发布结果。在最终的Capstone项目中,您将运用通过使用实际数据构建数据产品而学到的技能。完成后,学生将有一个档案袋,以证明他们对材料的掌握。

课程大纲

Course: 1

Course Link: https://www.coursera.org/learn/data-scientists-tools?specialization=jhu-data-science

Title:The Data Scientist’s Toolbox

Description:In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Course: 2

Course Link: https://www.coursera.org/learn/r-programming?specialization=jhu-data-science

Title:R Programming

Description:In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Course: 3

Course Link: https://www.coursera.org/learn/data-cleaning?specialization=jhu-data-science

Title:Getting and Cleaning Data

Description:Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Course: 4

Course Link: https://www.coursera.org/learn/exploratory-data-analysis?specialization=jhu-data-science

Title:Exploratory Data Analysis

Description:This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

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课程简介

约翰霍普金斯大学的数据科学专项课程系列(Data Science Specialization),这个系列课程有10门子课程,包括数据科学家的工具箱,R语言编程,数据清洗和获取,数据分析初探,可重复研究,统计推断,回归模型,机器学习实践,数据产品开发,数据科学毕业项目,感兴趣的同学可以关注: Launch Your Career in Data Science-A nine-course introduction to data science, developed and taught by leading professors.

课程标签

数据产品 回归模型 可重复研究 统计推断 数据分析 R编程 R R语言 数据科学 数据清洗 机器学习实践 机器学习 数据获取

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