Python培训
Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。
NobleProg现场Python培训课程通过动手实践向学员演示Python编程语言的各个方面。本课程涉及的主题包括Python编程基础、高级Python编程、Python用于测试自动化、Python脚本和自动化，以及Python用于金融、银行、保险等领域的数据分析和大数据应用。
NobleProg Python培训课程还涵盖了用于机器学习和深度学习的Python库和框架的初级和高级课程。Python培训有多种形式可供选择，包括现场实时培训和使用交互式远程桌面设置的实时讲师指导培训。
Python现场培训可以在客户所在场所或NobleProg位于当地的培训中心进行。
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Python大纲
| 代码 | 名字 | 时长 | 概览 |
|---|---|---|---|
| mlfunpython | Machine Learning Fundamentals with Python | 14小时 | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28小时 | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nltk | Natural Language Processing with Python | 28小时 | This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking. |
| pythoncomputervision | 用Python实现计算机视觉 | 14小时 | 计算机视觉是一个涉及从数字媒体自动提取、分析和理解有用信息的领域。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。 在这一由讲师引导的现场培训中，学员将使用Python逐步创建简单的计算机视觉应用程序，并从中习得计算机视觉的基础知识。 在本次培训结束后，学员将能够： 了解计算机视觉的基础知识 使用Python来实现计算机视觉任务 使用Python构建自己的计算机视觉应用程序 受众 对计算机视觉感兴趣的Python程序员 课程形式 部分讲座、部分讨论、练习和大量实操 |
| mlfsas | Machine Learning Fundamentals with Scala and Apache Spark | 14小时 | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| pythonbigdata | Analyzing Big Financial Data with Python | 35小时 | Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn how to use Python for quantitative finance. By the end of this training, participants will be able to: Understand the fundamentals of Python programming Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics Implement financial algorithms using performance Python Audience Developers Quantitative analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 3627 | Introduction to Programming | 35小时 | The purpose of the training is to provide a basis for programming from the ground up to the general syntax of programming paradigms. The training is supported by examples based on programming languages such as C, Java, Python, Scala, C #, Closure and JavaScript. During the training, participants gain a general understanding of both the programming patterns, best practices, commonly used design and review of the implementation of these topics through various platforms. Each of the issues discussed during the course are illustrated with examples of both the most basic and more advanced and based on real problems. |
| datapyth | Data Analysis in Python using Pandas and Numpy | 14小时 | Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. |
| microservicespython | Developing Microservices with Python | 7小时 | Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices. In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python. By the end of this training, participants will be able to: Understand the basics of building microservices Learn how to use Python to build microservices Learn how to use Docker to deploy Python based microservices Audience Developers Programmers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonautomation | Python：自动化枯燥的事物 | 14小时 | 这一由讲师引导的培训是基于Al Sweigart所著的知名书籍——“用Python自动化枯燥的事物（Automate the Boring Stuff with Python）”。它针对初学者，通过实际操作练习和讨论涵盖了Python编程的基本概念。重点在于学习编写代码以显着提高办公效率。 在本次培训结束后，参与者将知道如何用Python进行编程，并将这项新技能应用于： 通过编写简单的Python程序来自动执行任务。 编写可以使用“正则表达式”进行文本模式识别的程序。 以编程方式生成和更新Excel电子表格。 解析PDF和Word文档。 抓取网站，并从线上来源提取信息。 编写发送电子邮件通知的程序。 使用Python的调试工具来快速解决错误。 以编程方式控制鼠标和键盘，以执行点击和输入。 受众 希望学习用Python编程的非程序员 希望优化办公效率的专业人士和公司团队 希望自动化繁琐程序和工作流程的经理 课程形式 部分讲座、部分讨论、练习和大量实操 |
| BigData_ | 数据分析和大数据的实用介绍 | 35小时 | 参与者完成此次培训后，将会对大数据及其相关技术、方法、工具有一个实际和真实的理解。 参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。 本课程首先介绍大数据的基本概念，然后讲解用于执行数据分析的编程语言和方法，最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。 受众 开发人员/程序员 IT顾问 课程形式 部分讲座、部分讨论、实操、偶尔测评进度 |
| tableaupython | Tableau with Python | 14小时 | Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: Integrate Tableau and Python using TabPy API Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mldlnlpintro | ML、DL與NLP入門與進階大綱 | 14小时 | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| openface | OpenFace: Creating Facial Recognition Systems | 14小时 | OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google’s FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to: Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation. Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| iotpython | Programming for IoT with Python | 14小时 | Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Python is a high-level programming language recommended for IoT due to its clear syntax and large community support. In this instructor-led, live training, participants will learn how to program IoT solutions with Python. By the end of this training, participants will be able to: Understand the fundamentals of IoT architecture Learn the basics of using Raspberry Pi Install and configure Python on Raspberry Pi Learn the benefits of using Python in programming IoT systems Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi Audience Developers Engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| restfulapi | Designing RESTful APIs | 14小时 | APIs (Application Programming Interface) allow for your application to connect with other applications. In this instructor-led, live training, participants will learn how to write high-quality APIs as they build and secure a backend API server. By the end of this training, participants will be able to: Choose from a number of frameworks for building APIs Understand and model the APIs published by companies such as Google and Facebook Create and publish their own Restful APIs for public consumption Secure their APIs through token-based authentication Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To customize this course for other languages, such as PHP, Javascript, etc., please contact us to arrange |
| pythonadvml | Python用于高级机器学习 | 21小时 | 在这一由讲师引导的现场培训中，参与者将学习Python中最相关及最尖端的机器学习技术，因为它们构建了一系列涉及图像、音乐、文本和财务数据的演示应用程序。 在本次培训结束后，参与者将能够： 运用用于解决复杂问题的机器学习算法和技术 将深度学习和半监督学习应用于涉及图像、音乐、文本和财务数据的应用程序 推动Python算法达到其最大潜力 使用例如NumPy和Theano的库和包 受众 开发人员 分析师 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| pythonexcel | Python for Excel | 14小时 | Python is a high-level programming language famous for its clear syntax and code readability. Excel is a spreadsheet application developed by Microsoft which is widely used in many industries. Adding Python to Excel makes it a powerful tool for data analytics. In this instructor-led, live training, participants will learn how to combine the capabilities of Python and Excel. By the end of this training, participants will be able to: Install and configure packages for integrating Python and Excel Read, write, and manipulate Excel files using Python Call Python functions from Excel Audience Developers Programmers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| pythonprog | Python Programming | 28小时 | This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages. The course can be delivered using Python 2.7.x or 3.x, with practical exercises making use of the full power of both versions of the language. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows). The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course. Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date. |
| pythontextml | Python：用文本进行机器学习 | 21小时 | 在这一由讲师引导的现场培训中，参与者将学习如何使用正确的机器学习和NLP（自然语言处理）技术从基于文本的数据中提取价值。 在本次培训结束后，参与者将能够： 用高质量、可重用的代码解决基于文本的数据科学问题 运用scikit-learn的不同方面（分类、聚类、回归、降维）来解决问题 使用基于文本的数据建立有效的机器学习模型 创建一个数据集并从非结构化文本中提取特征 用Matplotlib可视化数据 构建和评估模型以获得洞察力 解决文本编码错误 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| chatbotpython | Building Chatbots in Python | 21小时 | ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to: Understand the fundamentals of building chatbots Build, test, deploy, and troubleshoot various chatbots using Python Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| kivy | Kivy: Building Android Apps with Python | 7小时 | Kivy is an open-source cross-platform graphical user interface library written in Python, which allows multi-touch application development for a wide selection of devices. In this instructor-led, live training participants will learn how to install and deploy Kivy on different platforms, customize and manipulate widgets, schedule, trigger and respond to events, modify graphics with multi-touching, resize the screen, package apps for Android, and more. By the end of this training, participants will be able to Relate the Python code and the Kivy language Have a solid understanding of how Kivy works and makes use of its most important elements such as, widgets, events, properties, graphics, etc. Seamlessly develop and deploy Android apps based on different business and design requirements Audience Programmers or developers with Python knowledge who want to develop multi-touch Android apps using the Kivy framework Android developers with Python knowledge Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nlg | Python for Natural Language Generation | 21小时 | Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to: Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting Select and organize source content, plan sentences, and prepare a system for automatic generation of original content Understand the NLG pipeline and apply the right techniques at each stage Understand the architecture of a Natural Language Generation (NLG) system Implement the most suitable algorithms and models for analysis and ordering Pull data from publicly available data sources as well as curated databases to use as material for generated text Replace manual and laborious writing processes with computer-generated, automated content creation Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pytest | Unit Testing with Python | 21小时 | Unit testing is a testing approach that tests individual units of source code by modifying their properties or triggering an event to confirm whether the outcome is as expected. PyTest is a full-featured, API-independent, flexible, and extensible testing framework with an advanced, full-bodied fixture model. In this instructor-led, live training, participants will learn how to use PyTest to write short, maintainable tests that are elegant, expressive and readable. By the end of this training, participants will be able to: Write readable and maintainable tests without the need for boilerplate code Use the fixture model to write small tests Scale tests up to complex functional testing for applications, packages, and libraries Understand and apply PyTest features such as hooks, assert rewriting and plug-ins Reduce test times by running tests in parallel and across multiple processors Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium Use Python to test non-Python applications Audience Software testers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nlp | Natural Language Processing with Deep Dive in Python and NLTK | 35小时 | By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein. |
| seleniumpython | Selenium结合Python用于测试自动化 | 14小时 | Selenium是一个开源库，用于跨多个浏览器自动化Web应用程序测试。Selenium像人一样与浏览器交互：通过单击链接、填写表单和验证文本。它是Web应用程序测试自动化最流行的工具。Selenium构建在WebDriver框架上，对许多脚本语言（包括Python）具有出色的绑定。 在本次培训中，学员将把Python的强大功能与Selenium结合起来，以自动化示例Web应用程序的测试。通过在实验室环境中将理论与实践相结合，学员将学习并操作如何用Python和Selenium自动化自己的Web测试项目。 受众 测试人员和开发人员 课程形式 部分讲座、部分讨论、大量实操 |
| mlbankingpython_ | 机器学习用于银行业务（使用Python） | 21小时 | 在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。Python将被用作编程语言。 参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| behave | Behave: BDD结合Python（针对Python使用Cucumber/Gherkin） | 7小时 | Behave是一个开源的、基于Python的BDD框架，用于以自然语言风格编写测试。 BDD（行为驱动开发）是一种敏捷软件开发技术，鼓励开发人员、QA和非技术业务人员在软件项目中进行协作。 本培训讲首先讨论BDD，以及Behave框架如何用于Web应用程序的BDD测试。学员将有充足的机会与讲师和其他学员进行交流，并在实验室环境中开展动手练习，运用所学的概念和策略。 在本次培训结束后，学员将对BDD和Behave有一个明确的理解，并获得在实际测试场景中使用这些技术和工具的必要实践。 受众 测试人员和开发人员 课程形式 重视实操。大部分概念是通过示例、练习和动手开发学到的。 |
| pythonfinance | Python用于财务工作 | 35小时 | Python是一门在金融行业拥有巨大声望的编程语言。最大的投资银行和对冲基金正在使用它来构建包括核心交易项目及风险管理系统在内的广泛的金融应用。 在这一由讲师引导的现场培训中，参与者将学习如何使用Python开发实际的应用程序以解决一些特定的财务相关的问题。 在本次培训结束后，参与者将能够： 了解Python编程语言的基础知识 下载、安装和维护用Python创建财务应用程序的最佳开发工具 选择和利用最合适的Python软件包和编程技术来组织、可视化和分析从各种来源（CSV、Excel、数据库、网站等）得来的财务数据。 构建解决资产配置、风险分析、投资绩效等相关问题的应用程序 故障排除、集成部署和优化他们的应用程序 受众 开发人员 分析师 宽客 课程形式 部分讲座、部分讨论、练习和大量实操 注意事项 该培训旨在为金融专业人士所面对的一些原则问题提供解决方案。但是，如果您有一个特定的主题、工具或技术想要附加或详细说明，请联系我们以作安排。 |
| pythonmultipurpose | 高级Python | 28小时 | 在这一由讲师引导的培训中，参与者将学习高级Python编程技术，包括如何将这种多功能语言应用于解决分布式应用、财务、数据分析和可视化、UI编程及维护脚本等领域的问题。 受众 开发人员 课程形式 部分讲座、部分讨论、练习和大量实操 注意事项 如果您想添加、移除或自定义本课程中的任一部分或主题，请联系我们以作安排。 |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28小时 | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to: Design and code DL for NLP using Python libraries Create Python code that reads a substantially huge collection of pictures and generates keywords Create Python Code that generates captions from the detected keywords Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing) Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| textsum | 用Python进行文本摘要 | 14小时 | 在Python机器学习中，文本摘要功能可以读取输入文本并生成文本摘要。这个功能可以从命令行或从Python API / 库中获得。一个令人兴奋的应用是执行摘要的快速创建；这对在做报告和演讲前需要审阅大量文本数据的组织特别有用。 在这一由讲师引导的现场培训中，学员将学习使用Python创建一个简单的可自动生成输入文本摘要的应用程序。 在本次培训结束后，学员将能够： 使用一个命令行工具来总结文本。 使用Python库设计和创建文本摘要代码。 评估三个Python摘要库：sumy 0.7.0、psisummarization 1.0.4、readless 1.0.17 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| mlfinancepython | 机器学习用于金融领域（使用Python） | 21小时 | 机器学习是人工智能的一个分支，指计算机可以在不被明确编程的情况下学习。 在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决财务的现实问题。Python将被用作编程语言。 参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些团队项目以将所学知识运用到实践中。 在本次培训结束后，参与者将能够： 了解机器学习的基本概念 了解机器学习在金融领域的应用和使用 使用Python机器学习开发自己的算法交易策略 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| ooppython | 学习使用Python的面向对象编程 | 14小时 | 面向对象编程（Object-Oriented Programming，OOP）是一种基于对象概念的编程范式。OOP更注重数据而不是注重逻辑。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。 在这一由讲师引导的现场培训中，学员将学习如何使用Python开始面向对象编程。 在本次培训结束后，学员将能够： 了解面向对象编程的基本概念 了解Python中的OOP语法 用Python编写自己的面向对象程序 受众 想了解面向对象编程的初学者 有兴趣学习用Python进行OOP的开发人员 有兴趣学习OOP的Python程序员 课程形式 部分讲座、部分讨论、练习和大量实操 |
| sparkpython | 用Spark和Python通过PySpark处理大数据 | 21小时 | Spark是一个用于查询、分析和转换大数据的数据处理引擎。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。PySpark允许用户将Spark与Python连接。 在这一由讲师引导的现场培训中，学员将通过实践练习学习如何使用Python和Spark一起分析大数据。 在本次培训结束后，学员将能够： 了解如何使用Spark和Python一起分析大数据 开展模拟真实世界环境的练习 用不同的工具和技术通过PySpark进行大数据分析 受众 开发人员 IT专业人士 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| progbio | Programming for Biologists | 28小时 | This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives. The course is designed and aimed for people without computer science background who want to learn programming. This course is suited for: Researchers dealing with biological data. Scientists who would like to learn how to automate everyday tasks and analyse data. Managers who want to learn how programming improves workflows and conducting projects. By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format. |
| drlpython | 用Python进行深度强化学习 | 21小时 | 深度强化学习是指“人工智能体”通过反复试验和奖惩来学习的能力。人工智能体旨在模仿人类直接从原始输入（如视觉）获取和构建知识的能力。为了实现强化学习，深度学习和神经网络会被用到。强化学习与机器学习不同，不依赖于有监督和无监督的学习方法。 在这一由讲师引导的现场培训中，学员将在逐步创建深度学习智能体的过程中学习深度强化学习的基础知识。 在本次培训结束后，学员将能够： 理解深度强化学习的基本概念，及其与机器学习的区别 运用先进的强化学习算法来解决实际问题 构建深度学习智能体 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
近期课程
| 课程 | 日期 | 价格【远程 / 传统课堂】 |
|---|---|---|
| Python Programming - Beijing Digital Building | 星期三, 2018-05-16 09:30 | ￥35330 / ￥36530 |
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