Data Science 简明教程
Data Science - Tools in Demand
数据科学工具用于深入挖掘原始且复杂的数据（非结构化或结构化数据），并使用统计学、计算机科学、预测建模和分析以及深度学习等不同的数据处理技术对其进行处理、提取和分析，从而找到有价值的见解。
Data Science tools are used to dig deeper into raw and complicated data (unstructured or structured data) and process, extract, and analyse it to find valuable insights by using different data processing techniques like statistics, computer science, predictive modelling and analysis, and deep learning.
数据科学家在数据科学生命周期的不同阶段使用各种工具，每天处理泽字节和尧字节的结构化和/或非结构化数据，并从中获取有用的见解。这些工具最重要的是，它们使得无需使用复杂的编程语言即可完成数据科学任务。这是因为这些工具具有已设置好的算法、函数和图形用户界面（GUI）。
Data scientists use a wide range of tools at different stages of the data science life cycle to deal with zettabytes and yottabytes of structured and/or unstructured data every day and get useful insights from it. The most important thing about these tools is that they make it possible to do data science tasks without using complicated programming languages. This is because these tools have algorithms, functions, and graphical user interfaces that are already set up (GUIs).
Best Data Science Tools
市场上有很多数据科学工具。因此，很难决定哪种工具最适合你的旅程和职业生涯。以下图表根据需要表示了一些最好的数据科学工具——
There are a lot of tools for data science in the market. So, it can be hard to decide which one is best for your journey and career. Below is the diagram that reperesents some of the best data science tools according to the need −
SQL
数据科学是对数据的综合研究。要访问数据并对其进行操作，必须从数据库中提取数据，为此需要 SQL。数据科学在很大程度上依赖于关系数据库管理。利用 SQL 命令和查询，数据科学家可以管理、定义、更改、创建和查询数据库。
Data Science is the comprehensive study of data. To access data and work with it, data must be extracted from the database for which SQL will be needed. Data Science relies heavily on Relational Database Management. With SQL commands and queries, a Data Scientist may manage, define, alter, create, and query the database.
一些当代领域采用 NoSQL 技术对其产品数据管理进行了装备，但对于许多商业智能工具和办公流程，SQL 仍然是最佳选择。
Several contemporary sectors have equipped their product data management with NoSQL technology, yet SQL remains the best option for many business intelligence tools and in-office processes.
DuckDB
DuckDB 是一款基于表格的关系型数据库管理系统，它还让您可以使用 SQL 查询来进行分析。它开源且免费，并拥有众多功能，例如更快的分析查询、更简单的操作等等。
DuckDB is a relational database management system based on tables that also lets you use SQL queries to do analysis. It is free and open source, and it has many features like faster analytical queries, easier operations, and so on.
DuckDB 还与数据科学中使用的 Python、R、Java 等编程语言配合使用。您可以使用这些语言来创建、注册并处理数据库。
DuckDB also works with programming languages like Python, R, Java, etc. that are used in Data Science. You can use these languages to create, register, and play with a database.
Beautiful Soup
Beautiful Soup 是一个 Python 库，可以从 HTML 或 XML 文件中提取信息或拉取信息。它是一个易于使用的工具，使您可以读取网站的 HTML 内容，从中获取信息。
Beautiful Soup is a Python library that can pull or extract information from HTML or XML files. It is an easy-to-use tool that lets you read the HTML content of websites to get information from them.
该库可帮助数据科学家或数据工程师设置自动网络抓取，这是完全自动数据管道中的一个重要步骤。
This library can help Data Scientists or Data Engineers set up automatic Web scraping, which is an important step in fully automated data pipelines.
它主要用于网络抓取。
It is mainly used for web scrapping.
Scrapy
Scrapy 是一款开源 Python 网络爬取框架，用于抓取大量网页。它是一款网络爬虫，可以爬取和抓取网络。它为您提供了从网站快速获取数据、根据需要处理数据，并以所需的结构和格式存储数据所需的全部工具。
Scrapy is an open-source Python web crawling framework that is used to scrape a lot of web pages. It is a web crawler that can both scrape and crawl the web. It gives you all the tools you need to get data from websites quickly, process them in the way you want, and store them in the structure and format you want.
Selenium
Selenium 是一个免费的开源测试工具，用于在不同的浏览器上测试网络应用程序。Selenium 只能测试网络应用程序，因此无法用于测试桌面或移动应用程序。Appium 和 HP 的 QTP 是可用于测试软件和移动应用程序的另外两个工具。
Selenium is a free, open-source testing tool that is used to test web apps on different browsers. Selenium can only test web applications, so it can’t be used to test desktop or mobile apps. Appium and HP’s QTP are two other tools that can be used to test software and mobile apps.
Python
数据科学家最常使用 Python，这也是最流行的编程语言。Python 在数据科学领域如此受欢迎的一个主要原因是它的易用性和简单的语法。这使得没有工程背景的人也能轻松学习和使用。此外，还有很多开源库和在线指南，用于执行数据科学任务，如机器学习、深度学习、数据可视化等。
Data Scientists use Python the most and it is the most popular programming language. One of the main reasons why Python is so popular in the field of Data Science is that it is easy to use and has a simple syntax. This makes it easy for people who don’t have a background in engineering to learn and use. Also, there are a lot of open-source libraries and online guides for putting Data Science tasks like Machine Learning, Deep Learning, Data Visualization, etc. into action.
python 中数据科学使用最频繁的一些库包括：
Some of the most commonly used libraries of python in data science are −
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Numpy
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Pandas
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Matplotlib
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SciPy
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Plotly
R
R 是数据科学中仅次于 Python 的第二常用的编程语言。它最初是为了解决统计问题，但现在已发展成为一个完整的数据科学生态系统。
R is the second most-used programming language in Data Science, after Python. It was first made to solve problems with statistics, but it has since grown into a full Data Science ecosystem.
大多数人使用库 Dpylr 和 readr 来加载数据并对其进行更改和添加。ggplot2 允许您使用不同的方法以图表方式显示数据。
Most people use Dpylr and readr, which are libraries, to load data and change and add to it. ggplot2 allows you use different ways to show the data on a graph.
Tableau
Tableau 是一个可视化分析平台，它正在改变人们和组织使用数据来解决问题的方式。它为人们和组织提供了充分利用其数据的所需工具。
Tableau is a visual analytics platform that is changing the way people and organizations use data to solve problems. It gives people and organizations the tools they need to extract the most out of their data.
在沟通方面，Tableau 非常重要。数据科学家通常需要分解信息，以便其团队、同事、经理和客户能够更好地理解信息。在这些情况下，信息需要易于查看和理解。
When it comes to communication, tableau is very important. Most of the time, Data Scientists have to break down the information so that their teams, colleagues, executives, and customers can understand it better. In these situations, the information needs to be easy to see and understand.
Tableau 帮助团队深入挖掘数据，找出通常隐藏在其中的见解，然后以既美观又易于理解的方式展示数据。Tableau 还有助于数据科学家快速浏览数据，在浏览数据的过程中添加和删除内容，最终以交互方式描述所有重要内容。
Tableau helps teams dig deeper into data, find insights that are usually hidden, and then show that data in a way that is both attractive and easy to understand. Tableau also helps Data Scientists quickly look through the data, adding and removing things as they go so that the end result is an interactive picture of everything that matters.
Tensorflow
TensorFlow 是一个开源且免费使用的机器学习平台，它使用数据流图。该图的节点是数学运算，边缘是流经它们的多维数据数组（张量）。它的架构非常灵活，机器学习算法可以描述为协同工作的运算图。可以在便携式设备、台式机和高端服务器等不同平台上的 GPU、CPU 和 TPU 上对其进行训练和运行，而无需更改代码。这意味着来自各种背景的程序员可以使用相同的工具进行合作，从而大大提高他们的生产力。Google 大脑团队创建该系统是为了研究机器学习和深度神经网络（DNN）。但是，该系统足够灵活，可用于广泛的其他领域。
TensorFlow is a platform for machine learning that is open-source, free to use and uses data flow graphs. The nodes of the graph are mathematical operations, and the edges are the multidimensional data arrays (tensors) that flow between them. The architecture is so flexible; machine learning algorithms can be described as a graph of operations that work together. They can be trained and run on GPUs, CPUs, and TPUs on different platforms, like portable devices, desktops, and high-end servers, without changing the code. This means that programmers from all kinds of backgrounds can work together using the same tools, which makes them much more productive. The Google Brain Team created the system to study machine learning and deep neural networks (DNNs). However, the system is flexible enough to be used in a wide range of other fields as well.
Scikit-learn
Scikit-learn 是一个易于使用的流行开源 Python 机器学习库。它拥有广泛的监督和无监督学习算法，以及用于模型选择、评估和数据预处理的工具。Scikit-learn 在学术界和商业中都被广泛使用。它以速度快、可靠且易于使用而著称。
Scikit-learn is a popular open-source Python library for machine learning that is easy to use. It has a wide range of supervised and unsupervised learning algorithms, as well as tools for model selection, evaluation, and data preprocessing. Scikit-learn is used a lot in both academia and business. It is known for being fast, reliable, and easy to use.
它还具有减少维度、选择特征、提取特征、使用集成技术和使用程序附带数据集的功能。我们将依次查看这些组件。
It also has features for reducing the number of dimensions, choosing features, extracting features, using ensemble techniques, and using datasets that come with the program. We will look at each of these things in turn.
Keras
Google 的 Keras 是一个用于创建神经网络的高级深度学习 API。它是用 Python 构建的，用于简化神经网络的构建。此外，它支持不同的后端神经网络计算。
Google’s Keras is a high-level deep learning API for creating neural networks. It is built in Python and is used to facilitate neural network construction. Moreover, different backend neural network computations are supported.
由于它提供具有高度抽象的 Python 接口和大量用于计算的后端，因此 Keras 相对容易理解和使用。这使得 Keras 比其他深度学习框架慢，但对初学者非常友好。
Since it offers a Python interface with a high degree of abstraction and numerous backends for computation, Keras is reasonably simple to understand and use. This makes Keras slower than other deep learning frameworks, but very user-friendly for beginners.
Jupyter Notebook
Jupyter Notebook 是一个开源在线应用程序，允许创建和共享带有实时代码、方程式、可视化效果和叙述性文本的文档。它在数据科学家和机器学习从业者中很受欢迎，因为它为数据探索和分析提供了一个交互式环境。
Jupyter Notebook is an open-source online application that allows the creation and sharing of documents with live code, equations, visualisations, and narrative texts. It is popular among data scientists and practitioners of machine learning because it offers an interactive environment for data exploration and analysis.
使用 Jupyter Notebook，您可以在网络浏览器中编写并运行 Python 代码（以及用其他编程语言编写的代码）。结果显示在同一文档中。这使您可以将代码、数据和文本说明全部放在一个地方，从而可以轻松地分享和复制您的分析结果。
With Jupyter Notebook, you can write and run Python code (and code written in other programming languages) right in your web browser. The results are shown in the same document. This lets you put code, data, and text explanations all in one place, making it easy to share and reproduce your analysis.
Dash
Dash 是数据科学的一个重要工具，因为它可以让您使用 Python 创建交互式网络应用程序。它使得创建数据可视化仪表盘和应用变得既轻松又快捷，而无需了解如何为网络编写代码。
Dash is an important tool for data science because it lets you use Python to create interactive web apps. It makes it easy and quick to create data visualisation dashboards and apps without having to know how to code for the web.