Introduction to Python for Data Science and Data Engineering - Mandarin Chinese

本课程专为 Python 零基础学员设计，旨在介绍以编程方式处理数据的基础知识。课程从编程表达式、变量和数据类型的基本介绍开始，随后逐步深入讲解条件语句与控制语句、函数、集合类型、循环、错误处理以及类。您将学习如何通过外部依赖库扩展 Python 的功能，并获得使用 Pandas 依赖库进行数据分析与可视化的实践经验，包括借助 Apache Spark 扩展 Pandas 工作流。最后，您将浏览云计算的基本原理。在整个课程中，您将通过实验室练习获得大量实践机会，并可参考额外的资源，在课后进一步深化编程知识。

Skill Level

Introductory

Duration

10h

Prerequisites

本内容是为具备以下技能、知识和能力的参与者而开发的：

• 基本的计算机使用能力及对操作系统的熟悉程度（文件管理、网络浏览器、软件安装）

• 对数学概念的初步理解（算术运算、基础代数、统计学基础）

• 对数据概念的初步了解（数据集、表格、行、列、数据类型）

• 熟悉网络浏览器和在线学习平台，以便访问 Databricks 工作区

• 具备基本的问题解决能力和逻辑思维，用于理解编程概念

• 无需具备 Python 使用经验；对其他编程语言中编程概念的一般性了解将有所帮助

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Customer registration Partner registration

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

Purchase now

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Engineer

Introduction to Python for Data Science and Data Engineering

This course is intended for complete beginners to Python to provide the basics of programmatically interacting with data. The course begins with a basic introduction to programming expressions, variables, and data types. It then progresses into conditional and control statements, functions, collection types, loops, error handling, and classes. You will learn to extend Python with external libraries and gain experience using the pandas library for data analysis and visualization, including scaling pandas workflows with Apache Spark. Lastly, you will explore the fundamentals of cloud computing. Throughout the course, you will gain hands-on practice through lab exercises, with additional resources to deepen your knowledge of programming after the class.

Machine Learning Practitioner

Machine Learning Model Deployment

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.

Note:

1. This is the third course in the 'Machine Learning with Databricks’ series.

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Paid & Subscription

Lab

Associate

Agent Evaluation on Databricks - Mandarin Chinese

本课程教授学生如何使用 MLflow 的评估框架对 AI 智能体进行系统性评估，以应对传统软件测试无法处理的非确定性 AI 系统所带来的独特挑战。学生将评估一个已注册在 Unity Catalog 中并通过 Unity Gateway 调用其模型的共享工具调用智能体，使用内置评判器处理正确性和安全性等常见标准，使用准则评判器满足特定业务需求，并使用自定义评判器应对专项需求。本课程涵盖使用精选数据集进行离线评估和在线生产监控，包括在 Unity Catalog 中存储 MLflow Traces。学生将使用 MLflow Tracing 了解智能体的执行模式，并收集来自开发人员和主题专家的人工反馈，这些专家通过 Databricks App 对智能体进行审查。通过实践演示和实验，学生将掌握创建评估 Workflows 的技能，从而在 AI 智能体开发生命周期中持续推动质量提升。

注意：对于 SCORM 讲授文件，请确保在完成内容后关闭 SCORM 窗口。请勿点击“Next Lesson”按钮，否则可能导致 SCORM 模块无法被标记为已完成。

Paid & Subscription

Lab

Associate