Introduction to Data Analytics with Microsoft Excel

所在平台: Udemy

课程主页: https://www.udemy.com/course/introduction-to-data-analytics-with-microsoft-excel/

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**课程名称:使用 Microsoft Excel 进行数据分析入门** **课程概述:** 本课程旨在为您提供数据分析的全面介绍,尤其侧重于使用 Microsoft Excel。数据分析被誉为21世纪最热门的职业之一。本课程将涵盖数据分析的核心概念、价值,并引导您通过实际操作将原始数据转化为有价值的洞察,并以引人入胜的故事形式呈现。您可以将此课程视为您迈向成为一名成熟数据科学家之旅的第一步。 **课程要求:** * Microsoft Office 365 或 Excel 2010 - 2019 * Mac 用户在使用数据透视表时,界面可能与示例略有不同。 * 具备 Excel 的基本功能操作经验会有帮助,但并非必需。 **课程大纲:** 本课程将详细涵盖以下主题,并提供数据集、模板以及17项实践活动,指导您一步步操作: 1. **什么是数据分析?** * 数据分析的定义 * 为何在新时代需要数据分析 2. **数据思考:** * 数据在理论环境中的运作方式 * 数据在实际应用中的运作方式 * 定性数据与定量数据及其重要性 3. **寻找您的数据:** * 数据来源的查找方法 * 数据源内容的了解 * 数据集的审查与上手实践 4. **分析您的数据:** * 平均值、众数、中位数和极差 * 正态数据与非正态数据及其对可预测性的影响 * 异常值识别与处理 * 分布与直方图的重要性 * 标准差与相对标准差,方差为何是“敌人” * 运行图和控制图的含义与解读 5. **使用数据透视表:** * 数据透视表构建器的工作原理 * 设置表头 * 计算字段 * 排序与筛选 * 通过数据透视表转换数据 6. **数据工程:** * 创建新的、有见地的“数据集” * 平衡数据的重点 * 质量 (Quality)、成本 (Cost) 和交付 (Delivery) 的整合分析 7. **开始讲述您的分析故事:** * 数据想要告诉您什么? * 提出问题 * 将数据转化为信息 8. **可视化您的数据:** * 报告的层级 * 选择合适的图表 * 颜色选择的重要性 * 可视化数据的实践 9. **呈现您的数据:** * 通过叙述将故事整合 10. **实际活动:** * 本课程将详细介绍以下实践活动: * 平均值、众数、中位数、极差与正态性分析 * 分布与直方图分析 * 标准差与相对标准差分析 * 基础数据工程实践 * 创建运行图 * 创建控制图 * 创建索赔数据的汇总数据透视表 * 数据转换实践 * 计算字段、排序与筛选实践 * QCD 数据工程实践 * 使用数据透视表回答分析问题 * 可视化数据实践 * 整合战略层面的分析 * 整合战术层面的分析 * 整合操作层面的分析 * 添加关键发现 * 添加建议 **适合人群:** * 任何经常使用 Excel 并希望提升技能的人 * 具备 Excel 基础技能,希望在数据探索与分析方面变得更熟练的用户 * 寻求全面、引人入胜且高度互动式培训的学生 * 任何希望从事数据分析或商业智能领域职业的人

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课程详情

RequirementsMicrosoft Office 365 or Excel 2010 - 2019Mac users Pivot Visuals may look slightly different to the examples shownBasic experience with Excel functionality is a bonus but not requiredDescriptionWelcome to the world of Data Analytics, voted the sexiest job of the 21st Century.In this expertly crafted course, we will cover a complete introduction to data analytics using Microsoft Excel, you will cover the concepts, the value and practically apply core analytical skills to turn data into insight and present as a story.Look at this as the first step in becoming a fully-fledged Data ScientistCourse OutlineThe course covers each of the following topics in detail, with datasets, templates and 17 practical activities to walk through step by step:What is Data AnalyticsWhy Do We Need It in this new worldThinking about Data, how it works in the lad v how it works in the wildQualitative v Quantitative data and their importanceFinding Your DataHow to find Sources of Data and what they containReviewing the Dataset and getting hands onAnalysing Your DataMean, Modes, Median and RangeNormal and Non normal Data and its impacts to predictabilityWhat is an Outlier in our data and how do we removeDistribution and Histograms and why they are importantStandard Deviation and Relative Standard Deviation, why variance is the enemyWhat are Run and Control charts and what do they tell us?Working With Pivot TablesHow the Pivot Builder WorksSetting Our HeadersWorking with calculated fieldsSorting and FilteringTransforming Data with Pivot TablesData EngineeringHow to create new, insightful datasetsThe importance of balanced dataLooking at Quality, Cost and Delivery togetherStart Telling Our Analytical StoryWhat is your data telling?Ask Yourself QuestionsTransforming Data into InformationVisualizing Your DataLevels of ReportingWhat Chart to UseDoes Color MatterLet's Visualize Some DataPresenting Your DataBringing The Story Together with a NarrativePractical ActivitiesWe will cover the following practical activities in detail through this course:Practical Example 1 - Mean, Mode, Median, Range & NormalityPractical Example 2 - Distribution and HistogramsPractical Example 3 - Standard Deviation and Relative Standard DeviationPractical Example 4 - A Little Data EngineeringPractical Example 5 - Creating a Run ChartPractical Example 6 - Create a Control ChartPractical Example 7 - Create a Summary Pivot of Our Claims DataPractical Example 8 - Transforming DataPractical Example 9 - Calculated Fields, Sorting and FilteringPractical Example 10 - Lets Engineer Some QCD DataPractical Example 11 - Lets Answer Our Analytical Questions with PivotsPractical Example 12 - Visualizing Our DataPractical Example 13 - Lets Pull our Strategic Level Analysis TogetherPractical Example 14 - Lets Pull our Tactical Level Analysis TogetherPractical Example 15 - Lets Pull our Operational Level Analysis TogetherPractical Example 16 - Lets Add Our Key FindingsPractical Example 17 - Lets Add Our RecommendationsWho this course is for:Anyone who works with Excel on a regular basis and wants to supercharge their skillsExcel users who have basic skills but would like to become more proficient in data exploration and analysisStudents looking for a comprehensive, engaging, and highly interactive approach to trainingAnyone looking to pursue a career in data analysis or business intelligence

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