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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-data-magic-power-bi-tableau-analytics/
课程评论：没有评论
课程名称：数据魔法掌握：Power BI + Tableau + SQL，数据分析 课程概述：数据分析是审查、清理、转换和解释数据的过程，以提取有意义的见解、模式和趋势。通过利用数据，数据分析帮助组织做出明智的决策并提高业务绩效。 课程内容： 1. **数据分析的重要性**：数据分析对企业进行数据驱动决策至关重要，能够帮助组织识别趋势和机会、提高运营效率、增强客户体验、制定明智的战略决策并预测未来结果。 2. **数据分析的类型**：主要有四种数据分析类型： - 描述性分析：总结历史数据以提供洞察。 - 诊断分析：分析数据以理解特定事件发生的原因。 - 预测性分析：利用历史数据预测未来结果。 - 规范性分析：基于预测分析推荐行动。 3. **数据分析的步骤**：通常包括数据收集、数据清理、数据探索、数据建模、数据可视化以及结果解读。 4. **数据分析工具**：包括Power BI、Tableau、Excel、Python（如Pandas和Matplotlib库）、R等。 5. **数据分析所需技能**：包括数据处理、统计分析、数据可视化、领域知识和问题解决能力，通信和团队合作等软技能同样重要。 6. **Power BI Desktop的下载与安装**：可以从微软官网下载Power BI Desktop，按说明进行安装。 7. **理解界面、选项和设置**：Power BI Desktop提供用户友好的界面，可自定义工作区、数据源和报告布局。 8. **数据源及导入样本数据**：可以将Power BI连接到各种数据源，如数据库、Excel文件和网络服务，并导入样本数据进行练习。 9. **数据理解与基本数据类型**：了解数据的结构、数据类型（如文本、数字、日期）以及如何清理和转换数据以进行分析。 10. **图表构建**：使用Power BI创建各种可视化图表，如条形图、折线图、饼图等，以有效地表示数据。 11. **视觉交互**：设定视觉之间的交互，使得选择一个视觉会影响其他视觉，从而创建动态报告。 12. **格式设置**：为图表、字体、颜色和背景进行格式设置，使报告更具视觉吸引力和易于理解。 13. **网络爬虫**：使用网络爬虫技术从网站提取数据，融入外部数据源。 14. **数据建模**：在表之间创建关系，定义度量，以便进行准确分析和报告。 15. **关系数据库管理系统（RDBMS）简介**：如SQL Server、MySQL、Oracle等可连接到Power BI以获取数据。 16. **地图可视化**：支持地理空间数据的可视化，适用于基于位置的分析。 17. **保存Power BI文件**：报告可保存为.pbix文件，其他人可以通过Power BI Desktop共享和打开。 18. **散点图及其他功能**：利用散点图可视化两个数值变量之间的关系，同时Power BI还提供过滤、深入查看和自定义视觉等功能。 19. **条件格式**：应用条件格式于图表，突出显示特定数据点。 20. **在报告视图中隐藏表格**：隐藏不希望在报告中展示的表格或字段，以简化工作区。 21. **分组、条形图、柱形图、带状图、小倍数**：提供多种可视化类型，展现数据的不同形式，例如分组数据、使用条形或柱形图、带状图及小倍数比较分析。 通过掌握这些主题，学习者将能够使用Power BI这一强大工具创建信息丰富且视觉吸引的报告，以支持数据驱动的决策。
Data Analytics: Data analytics is the process of examining, cleaning, transforming, and interpreting data to extract meaningful insights, patterns, and trends. It helps organizations make informed decisions and improve their business performance by leveraging data.Why Data Analytics: Data analytics is crucial for businesses to make data-driven decisions. It enables organizations to:Identify trends and opportunities.Improve operational efficiency.Enhance customer experience.Make informed strategic decisions.Predict future outcomes.Types of Data Analytics: There are four main types of data analytics:Descriptive Analytics: Summarizes historical data to provide insights.Diagnostic Analytics: Analyzes data to understand why certain events occurred.Predictive Analytics: Uses historical data to predict future outcomes.Prescriptive Analytics: Recommends actions based on predictive analytics.Steps Involved in Data Analytics: The typical steps in data analytics include data collection, data cleaning, data exploration, data modeling, data visualization, and interpretation of results.Tools for Data Analytics: There are various tools for data analytics, including Power BI, Tableau, Excel, Python (with libraries like Pandas and Matplotlib), R, and more.Skills for Data Analytics: To excel in data analytics, you should have skills in data manipulation, statistical analysis, data visualization, domain knowledge, and problem-solving. Soft skills like communication and teamwork are also essential.Downloading and Installing Power BI Desktop: You can download Power BI Desktop from the official Microsoft website. After downloading, run the installer and follow the installation instructions.Understanding Interface, Options, and Settings: Power BI Desktop has a user-friendly interface with options and settings to customize your workspace, data sources, and report layouts.Data Source and Uploading Sample Data: You can connect Power BI to various data sources, such as databases, Excel files, and web services. You can also import sample data to practice.Understanding Data and Basic Data Types: In Power BI, you must understand your data's structure, data types (e.g., text, numbers, dates), and how to clean and transform it for analysis.Building Charts: Power BI allows you to create various visualizations, including bar charts, line charts, pie charts, and more, to represent your data effectively.Visual Interactions: You can set up interactions between visuals to create dynamic reports where selecting one visual influences others.Formatting: You can format visuals, fonts, colors, and backgrounds to make your reports visually appealing and easier to understand.Web Scraping: Power BI can extract data from websites using web scraping techniques, allowing you to incorporate external data sources.Data Modeling: You can create relationships between tables, define measures, and build a data model for accurate analysis and reporting.RDBMS Introduction: Relational Database Management Systems (RDBMS) like SQL Server, MySQL, and Oracle can be connected to Power BI to fetch data.Map Visualization: Power BI supports mapping visualizations to display data geospatially, making it useful for location-based analysis.Saving Power BI Files: You can save your Power BI reports as.pbix files, which can be shared and opened by others using Power BI Desktop.Scatter Plot and Other Features: Scatter plots are useful for visualizing the relationship between two numeric variables. Power BI also offers many other features like filtering, drill-through, and custom visuals.Conditional Formatting: You can apply conditional formatting to visuals to highlight specific data points based on predefined rules.Hiding Tables in Report View: You can hide tables or fields that you don't want to display in the report view to declutter your workspace.Grouping, Bar Charts, Column Charts, Ribbon Charts, Small Multiples: These are various visualization types that you can create in Power BI to represent your data in different ways, such as grouping data, using bar or column charts, ribbon charts, and displaying small multiples for comparative analysis.Power BI is a powerful tool for data analytics, and mastering these topics will help you create informative and visually appealing reports for data-driven decision-making.
