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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/open-source-tools-for-data-science
课程评论：没有评论
课程名称：数据科学工具 课程概述： 在这门课程中，您将学习一些最流行的数据科学工具及其使用方法和特点。涵盖的工具包括 Jupyter Notebooks、JupyterLab、RStudio IDE、Git、GitHub 以及 Watson Studio。课程将介绍每个工具的用途、支持的编程语言以及它们的特点和局限性。通过在 Skills Network Labs 的云环境中，您将能测试每个工具，并按照指导在 Python、R 或 Scala 中运行简单代码。课程结束时，您将使用 IBM Watson Studio 创建一个最终项目，通过 Jupyter Notebook 展示您在准备 Notebook、编写 Markdown 和分享工作上的熟练程度。 课程大纲： 1. 数据科学工具概览：介绍数据科学工具的种类和分类，以及常见示例，包括开源、基于云和商业工具选项。 2. 数据科学编程语言：针对初学者，介绍各种编程语言的选择与应用，包括 Python、R、SQL 及其他常用语言（如 Java、Scala、C++、JavaScript 和 Julia）。 3. 包、API、数据集和模型：学习数据科学中的各种库，理解 API 在 REST 请求和响应中的作用，探索开放数据集，并学习如何使用机器学习模型解决问题。 4. Jupyter Notebooks 和 JupyterLab：介绍 Jupyter Notebook 和 Jupyter Lab 的功能，学习如何在 Notebook 会话中使用不同的内核，以及 Anaconda Jupyter 环境中的工具。 5. RStudio 和 GitHub：学习 R 编程语言和 RStudio 的基本用法，掌握数据可视化和 Git、GitHub 的使用技能，包括账户创建、项目库管理和工作流。 6. 创建和分享 Jupyter Notebook：在最终项目中展示课程所学的技能，并测试您对数据科学工具的理解。 7. [可选] IBM Watson Studio：学习 Watson Studio 和 IBM Cloud Pak 的使用，创建项目和 Jupyter Notebook，加载数据文件，并将笔记本连接到 GitHub。 通过本课程，您将掌握数据科学工具的基础知识，提升在数据科学领域的实践能力。
Name:Overview of Data Science Tools
Description:In this module, you will learn about the different types and categories of tools that data scientists use and popular examples of each. You will also become familiar with Open Source, Cloud-based, and Commercial options for data science tools.
Name:Languages of Data Science
Description:For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.
Name:Packages, APIs, Data Sets, and Models
Description:In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.
Name: Jupyter Notebooks and JupyterLab
Description:With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.
Name:RStudio & GitHub
Description:R is a statistical programming language and is a powerful tool for data processing and manipulation. This module will start with an introduction to R and RStudio. You will learn about the different R visualization packages and how to create visual charts using the plot function. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. While there are many distributed versioning systems, Git is amongst the most popular ones. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.
Name:Create and Share your Jupyter Notebook
Description:In this module, you will work on a final project to demonstrate some of the skills learned in the course. You will also be tested on your knowledge of various components and tools in a Data Scientist's toolkit learned in the previous modules.
Name:[Optional] IBM Watson Studio
Description:Watson Studio is a collaborative platform for the data science community and is used by Data Analysts, Data Scientists, Data Engineers, Developers, and Data Stewards to analyze data and construct models. In this module, you will learn about Watson Studio and IBM Cloud Pak for data as a service. Then you will create an IBM Watson Studio service and a project in Watson Studio. After creating the project, you will create a Jupyter notebook and load a data file. You will also explore the different templates and kernels in a Jupyter notebook. Finally, you will connect your Watson Studio account to GitHub and publish the notebook in GitHub. Note: This part of the course is optional and is not a mandatory requirement to complete the lab provided in this week of the course.
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
