Module 01 · Overview of Data Science Tools

Learning goals for the course课程学习目标

In this course you will be introduced to a Data Scientist's workbench or toolkit that consists of a variety of tools, languages, libraries, APIs, data sets, models, etc. used by Data Scientists. Try not to be overwhelmed by the sheer number of components and tools that exist in the Data Science ecosystem. The main goal of the course is for you to be knowledgeable about the kinds of tools Data Scientists use, their examples, and get some hands-on time with a few key tools.
在这个课程中，您将了解到数据科学家的工具体或工具包，它由数据科学家使用的一系列工具、语言、库、API、数据集、模型等组成。尽量不要被数据科学生态系统中存在的众多组件和工具所淹没。本课程的主要目标是让您了解数据科学家使用的工具类型、示例，并让您有机会接触一些关键工具。

As such, you are not required to recall the name of every single tool covered in the course. However, be familiar with the categories or types of tools and 1 or 2 examples each type. Modules 4 and 5 of this course will cover some of the most important ones for a beginner Data Scientist in greater depth and enable you to get hands-on experience with them. As you take additional Data Science courses, you will become more acquainted with some of the other tools and libraries. Some may be required to perform more specialized or advanced Data Science or Machine Learning tasks. So don't try to remember all of the names just now. Pay special attention, though, to Video and Lesson summaries.
因此，您不需要记住课程中涵盖的每一个工具的名称。但是，您需要熟悉工具的类别或类型，以及每种类型中的 1 到 2 个示例。本课程的第 4 和第 5 模块将更深入地介绍对初学者数据科学家来说最重要的工具之一，并使您能够获得实际操作经验。随着您学习更多的数据科学课程，您将更加熟悉其他一些工具和库。有些工具可能需要执行更专业或更高级的数据科学或机器学习任务。所以现在不要试图记住所有的名称。不过，请特别关注视频和课程摘要。

To successfully complete the course, you are required to complete the first 6 out of the 7 modules in the course. The 7th module is an optional one.
要成功完成本课程，您需要完成课程中的前 7 个模块中的 6 个。第 7 个模块是可选的。

Here is what you will be learning in each module:
以下是每个模块中您将学习的内容：

Module 1: Overview of Data Science Tools
模块 1：数据科学工具概述

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.
在本模块中，您将了解数据科学家使用的不同类型和类别的工具以及每个类型的流行实例。您还将熟悉数据科学工具的开源、云基础和商业选项。

Describe the components of a Data Scientist's toolkit and list various tool categories
描述数据科学家工具包的组成部分并列出各种工具类别
List examples of Open Source, Commercial, and Cloud-based tools in various categories
列举开源、商业和基于云的各种类别的工具示例

Module 2: Languages of Data Science
模块 2：数据科学语言

This module will bring awareness about the criteria determining 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 for more information about the languages.
本模块将介绍决定您应该学习哪种语言的标准。您将学习 Python、R、SQL 以及其他常见语言（如 Java、Scala、C++、JavaScript 和 Julia）的优势。您将探索如何使用这些语言进行数据科学。您还将查看一些关于这些语言的网站获取更多信息。

Learning Objectives 学习目标

Identify the criteria and roles for determining the language to learn.
确定学习语言的准则和角色。
Identify the users and benefits of Python.
确定 Python 的用户及其优势。
Identify the users and uses of the R language.
确定 R 语言的用户及其用途。
Define SQL elements and list their benefits.
定义 SQL 元素并列举其优势。
Review languages such as Java, Scala, C++, JavaScript, and Julia.
复习 Java、Scala、C++、JavaScript 和 Julia 等语言。
List the global communities for connecting with other users.
列出全球社区，以便与其他用户建立联系。

Module 3: Packages, APIs, Data Sets and Models
模块 3：软件包、API、数据集和模型

This module will give you in-depth knowledge of different libraries, APIs, dataset sources and models used by data scientist.
本模块将为您提供关于数据科学家使用的不同库、API、数据集来源和模型的深入知识。

Learning Objectives 学习目标

List examples of the various libraries: scientific, visualization, machine learning, and deep learning.
列出各种库的示例：科学、可视化、机器学习和深度学习库。
Define REST API to request and respond.
定义用于请求和响应的 REST API。
Describe data sets and sources of data.
描述数据集和数据来源。
Explore open data sets on the Data Asset eXchange.
在数据资产交易所探索开放数据集。
Describe how to use a learning model to solve a problem.
描述如何使用学习模型解决问题。
List the tasks that a data scientist needs to perform to build a model.
列出数据科学家需要执行的任务来构建模型。
Explore ML models in the Model Learning eXchange.
在模型学习交换中探索机器学习模型。

Module 4: Jupyter Notebooks and JupyterLab
模块 4：Jupyter Notebook 和 JupyterLab

This module introduces the Jupyter Notebook and JupyterLab. You will learn how to work with different kernels and the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module overviews cloud-based Jupyter environments and their data science features.
本模块介绍了 Jupyter Notebook 和 JupyterLab。您将学习如何使用不同的内核以及 Jupyter 的基本架构。此外，您将识别 Anaconda Jupyter 环境中的工具。最后，本模块概述了基于云的 Jupyter 环境及其数据科学功能。

Learning Objectives 学习目标

Describe how to use the notebooks in JupyterLab.
描述如何在 JupyterLab 中使用笔记本。
Describe how to work in a notebook session.
描述如何在笔记本会话中工作。
Describe the basic Jupyter architecture.
描述 Jupyter 的基本架构。
Describe how to work with kernels.
描述如何使用内核。
Identify tools in Anaconda Jupyter environments.
识别 Anaconda Jupyter 环境中的工具。
Describe cloud-based Jupyter environments and their data science features.
描述基于云的 Jupyter 环境及其数据科学功能。

Module 5: RStudio and GitHub
模块 5：RStudio 和 GitHub

This module will start with an introduction to R and RStudio and will end up with Github usage. You will learn about the different R visualization packages and how to create visual charts using the plot function.
本模块将从 R 和 RStudio 的介绍开始，最终以 Github 的使用结束。您将学习不同的 R 可视化包以及如何使用 plot 函数创建可视化图表。

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, creating a GitHub account and a project repository, adding files, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches, pull requests (PRs), and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.
在模块的后续部分，您将发展使用 Git 和 GitHub 所需的基本概念和动手技能。您将首先了解 Git 和 GitHub，创建 GitHub 账户和项目仓库，添加文件，并使用网页界面提交更改。接下来，您将熟悉涉及分支、拉取请求（PRs）和合并的 Git 工作流程。您还将完成一个项目，以应用和展示您新获得的知识和技能。

Learning Objectives 学习目标

Describe R capabilities and RStudio environment.
描述 R 的功能和 RStudio 环境。
Use the inbuilt R plot function.
使用内置的 R plot 函数。
Explain version control and describe the Git and GitHub environment.
解释版本控制并描述 Git 和 GitHub 环境。
Describe the purpose of source repositories and explain how GitHub satisfies the needs of a source repository.
描述源代码仓库的目的，并解释 GitHub 如何满足源代码仓库的需求。
Create a GitHub account and a project repository.
创建 GitHub 账户和项目仓库。
Demonstrate how to edit and upload files in GitHub.
演示如何在 GitHub 中编辑和上传文件。
Explain the purpose of branches and how to merge changes.
解释分支的目的以及如何合并更改。

Module 6: Final Project and Assessment
模块 6：最终项目和评估

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.
在本模块中，您将完成一个最终项目，以展示课程中学习的一些技能。您还将接受对先前模块中学习的数据科学家工具包中各种组件和工具的知识测试。

Learning Objectives 学习目标

Create a Jupyter Notebook with markdown and code cells
创建一个包含 markdown 和代码单元的 Jupyter Notebook
List examples of languages, libraries and tools used in Data Science
列举数据科学中使用的语言、库和工具的示例
Share your Jupyter Notebook publicly on GitHub
在 GitHub 上公开分享你的 Jupyter Notebook
Evaluate notebooks submitted by your peers using the provided rubric
使用提供的评分标准评估你同伴提交的 Notebook
Demonstrate proficiency in Data Science toolkit knowledge
展示在数据科学工具包知识方面的熟练程度

Module 7: IBM Watson Studio
模块 7：IBM Watson Studio

This is as an optional module if you are interested in learning about and working with data science tools from IBM such as Watson Studio.
这是一个可选模块，如果你有兴趣学习并使用 IBM 的数据科学工具，如 Watson Studio。

Learning Objectives 学习目标

Find common resources in Watson Studio and IBM Cloud Pak for Data.
在 Watson Studio 和 IBM Cloud Pak for Data 中查找常用资源。
Create an IBM Cloud account, service, and project in Watson Studio.
在 Watson Studio 中创建 IBM Cloud 账户、服务和项目。
Create and share a Jupyter Notebook.
创建和分享一个 Jupyter Notebook。
Use different types of Jupyter Notebook templates and kernels on IBM Watson Studio.
在 IBM Watson Studio 上使用不同类型的 Jupyter Notebook 模板和内核。
Describe how to connect a Watson Studio account and publish a notebook in GitHub.
描述如何连接 Watson Studio 账户并在 GitHub 上发布笔记本。