|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/pythonbi/
课程评论：没有评论
课程名称：使用Python进行商业智能层开发 课程概述：踏上数据分析与可视化的旅程，通过我们的综合课程“数据分析与可视化：从数据源到洞察”，您将获得利用数据进行明智决策和深入分析所需的知识与技能。 第一部分：“数据源层”将教您如何从各种来源获取数据，包括No-SQL数据库、CSV文件、电子表格、文本、HTML和PDF文件，并连接数据库服务器以访问远程数据。 第二部分：“数据准备层 - ETL”专注于通过数据框操作、处理字符串、日期和时间来准备数据分析，并使用Oracle PL SQL等技术远程转换数据。 第三部分：“数据可视化”将向您展示如何创建标准和交互式图表、可视化数据集合，并通过可视化技术分析客户行为。 第四部分：“数据分析”深入探讨数据分析的核心内容，涵盖数据分析周期、基础统计、线性回归、线性规划及完整的数据分析案例，包括证券分析。 第五部分：“数据共享”研究共享数据的方法，包括从命令行启动服务器、配置局域网中的Jupyter Notebook服务器、保护笔记本服务器以及将HTML和外部网络源整合到Python代码中。 第六部分：“商业智能背景”探讨商业智能的背景，研究与BI相关的Python主题，讨论各种数据类型，并在Power BI中扩展Python脚本，包括从Excel、SQL Server和网络源获取数据。 无论您是希望探索数据分析世界的初学者，还是想提高技能的经验丰富的专业人士，“数据分析与可视化：从数据源到洞察”都提供了全面且实用的学习体验，帮助您释放数据驱动洞察的全部潜力。
Embark on a journey into the world of data analytics and visualization with our comprehensive course, "Data Analytics and Visualization: From Sources to Insights." This course is meticulously crafted to equip you with the knowledge and skills needed to harness the power of data for informed decision-making and insightful analysis.In Section 1, "Data Sources Layer," you'll learn how to fetch data from various sources, including No-SQL databases, files such as CSV, spreadsheets, text, HTML, and PDF, as well as connect to database servers and access remote data.Section 2, "Data Preparation Layer - ETL," focuses on preparing data for analysis through operations on data frames, handling strings, dates, and times, and transforming data remotely using techniques such as Oracle PL SQL.Moving on to Section 3, "Data Visualization," you'll discover how to create standard and interactive charts, visualize sets, and analyze customer behavior through visualization techniques.Section 4, "Data Analytics," delves into the core of data analysis, covering the data analysis cycle, basics of statistics, linear regression, linear programming, and complete data analysis cases, including securities analysis.Section 5, "Data Sharing," explores techniques for sharing data, including starting servers from the command line, configuring Jupyter Notebook servers in a LAN, securing notebook servers, and integrating HTML and external web sources into Python code.In Section 6, "Business Intelligence Context," you'll delve into the context of business intelligence, explore Python topics relevant to BI, discuss different types of data, and extend Python scripts in Power BI, including getting data from Excel, SQL Server, and web sources.Whether you're a beginner looking to explore the world of data analytics or an experienced professional seeking to enhance your skills, "Data Analytics and Visualization: From Sources to Insights" provides a comprehensive and practical learning experience to help you unlock the full potential of data-driven insights.
