[课程] 万门大学-13-商业数据分析特训班

作者：观棋时间：24-08-09 阅读数：109人阅读

本套课程来自万门大学：商业数据分析特训班，从实际解决商业问题，由Joshua和Nick共同授课，搭建商业数据分析知识框架结构，突破原有的学习瓶颈，通过对数据的分析解读，能够解决一些简单的商业问题。本课程分为56讲，课程大小共有25G，官方售价为3998元。文章底部附下载链接。

课程介绍：

本课程内容涵盖Excel数据分析、Python基础、基础统计学、常见的数据分析方法、SQL等商业数据知识点介绍。本着循序渐进的原则，均从基础知识开始讲起，逐步深入，带你实现从入门到精通的转变。本着实用性原则，从实际业务理解开始讲起，告诉你如何基于业务数据，通过分析手段挖掘其背后的问题或隐含的商业价值，从而解决实际商业问题！

什么是商业数据分析？
·从业要求=基础知识+工具+业务能力+软技能
·基于数据通过分析手段挖掘出商业价值，解决商业问题

适合人群：
希望进入商业数据分析领域的学员
想要学习更多商业数据职场人士
对商业数据分析感兴趣的学员
官方课程链接：https://www.wanmen.org/courses/5c243172108a27759584d9bd/

课程目录：

目录：万门教育：商业数据分析特训班 [25G]
1.1商业数据分析引入.mp4 [31.8M]
1.2什么是商业数据分析？.mp4 [16.1M]
1.3所需技能.mp4 [48.5M]
1.4基本分析流程及供应链各个环节.mp4 [27.8M]
1.5商业理解.mp4 [24.3M]
1.6答疑（一）.mp4 [24M]
1.7数据粒度（一）.mp4 [25.3M]
1.8数据粒度（二）.mp4 [19.6M]
1.9数据粒度（三）.mp4 [26.7M]
1.10数据粒度（四）.mp4 [38.3M]
1.11答疑（二）.mp4 [19.7M]
1.12答疑（三）.mp4 [20.1M]
1.13答疑（四）.mp4 [39.8M]
2.1数据质量与形式.mp4 [52.9M]
2.2数据隐性.mp4 [32.4M]
2.3案例分析.mp4 [32.8M]
2.4不同类型的分析.mp4 [24.3M]
2.5数据可视化.mp4 [72M]
2.6典型数据驱动开发团队的人员.mp4 [27M]
2.7答疑.mp4 [12.3M]
3.10查找和替换（一）.mp4 [60.8M]
3.11查找和替换（二）.mp4 [68.5M]
3.12答疑.mp4 [53.1M]
3.1Excel简介.mp4 [97M]
3.2Excel基本操作（一）.mp4 [94M]
3.3Excel基本操作（二）.mp4 [54M]
3.4Excel基本操作（三）.mp4 [39.7M]
3.5行列及区域（一）.mp4 [69.7M]
3.6行列及区域（二）.mp4 [68M]
3.7数据及数据类型（一）.mp4 [69.4M]
3.8数据及数据类型（二）.mp4 [57.6M]
3.9数据及数据类型（三）.mp4 [95.8M]
4.1答疑回顾.mp4 [62.1M]
4.2排序.mp4 [179.3M]
4.3排序插入.mp4 [38.3M]
4.4筛选（一）.mp4 [149M]
4.5筛选（二）.mp4 [95.8M]
4.6答疑.mp4 [67.7M]
5.10报名统计.mp4 [32.1M]
5.11SUMIF.mp4 [68.6M]
5.12SUMIF练习.mp4 [39M]
5.1分类汇总（一）.mp4 [187M]
5.2分类汇总（二）.mp4 [35.3M]
5.3公式与函数（一）.mp4 [47M]
5.4公式与函数（二）.mp4 [23.7M]
5.5公式与函数（三）.mp4 [63.6M]
5.6逻辑判断IF（一）.mp4 [71.3M]
5.7逻辑判断IF（二）.mp4 [76.4M]
5.8COUNTIF.mp4 [111.8M]
5.9重复.mp4 [31.6M]
6.1VLOOKUP.mp4 [46.4M]
6.2菜单、Join Two Tables.mp4 [25.5M]
6.3记录多匹配、跨表.mp4 [103.5M]
6.4跨表、跨文件薄.mp4 [74.1M]
6.5示例：王者荣耀、打标签.mp4 [29.8M]
6.6文本vlookup、Hlookup.mp4 [57.3M]
6.7Match&Index.mp4 [34.8M]
6.8返回多列.mp4 [53.8M]
6.9认识数组、记录多匹配.mp4 [87.2M]
7.1商务智能含义（一）.mp4 [15.5M]
7.2商务智能含义（二）.mp4 [46M]
7.3数据仓库系统.mp4 [58.7M]
7.4常见BI.mp4 [24.9M]
7.5Power BI（一）.mp4 [69.9M]
7.6Power BI（二）.mp4 [136.3M]
7.7答疑.mp4 [85.6M]
8.10Python for basic data type（一）.mp4 [61.4M]
8.11Python for basic data type（二）.mp4 [31.6M]
8.12Python for basic data type（三）.mp4 [43.1M]
8.13Python for basic data type（四）.mp4 [39.2M]
8.14Python for basic data type（五）.mp4 [38.6M]
8.15Python for basic data type（六）.mp4 [26.6M]
8.16Python for basic data type（七）.mp4 [28.5M]
8.17Python for basic data type（八）.mp4 [66.2M]
8.1Python基础课程.mp4 [18.7M]
8.2Python能做什么.mp4 [31.4M]
8.3Python20载.mp4 [78.6M]
8.4Python简单介绍.mp4 [38.3M]
8.5工具安装及环境配置（一）.mp4 [27.4M]
8.6工具安装及环境配置（二）.mp4 [31.4M]
8.7计算机与程序思维.mp4 [34M]
8.8Jupyter notebook（一）.mp4 [51.9M]
8.9Jupyter notebook（二）.mp4 [65.1M]
9.10王者荣耀case function（三）.mp4 [153.1M]
9.11Quiz—基本语法及变量.mp4 [44.2M]
9.12Way to Function（一）.mp4 [37.9M]
9.13Way to Function（二）.mp4 [65.4M]
9.14Quiz—Code Structure（一）.mp4 [20.9M]
9.15Quiz—Code Structure（二）.mp4 [49.7M]
9.16Python basic data structure（一）.mp4 [44.2M]
9.17Python basic data structure（二）.mp4 [33.5M]
9.18Python basic data structure（三）.mp4 [50.4M]
9.1答疑.mp4 [47.3M]
9.2Python for basic data type（一）.mp4 [70.1M]
9.3Python for basic data type（二）.mp4 [50.3M]
9.4Quiz—字符串.mp4 [27.9M]
9.5Python Code Structure.mp4 [51.5M]
9.6While Loop.mp4 [39.7M]
9.7For Loop.mp4 [39.2M]
9.8王者荣耀case function（一）.mp4 [97.1M]
9.9王者荣耀case function（二）.mp4 [61.6M]
10.10Zip.mp4 [30.3M]
10.11Mutable，Immutable.mp4 [72.9M]
10.12Traversal Sequence Data Structure.mp4 [35.5M]
10.13函数进阶（一）.mp4 [35.6M]
10.14函数进阶（二）.mp4 [62.8M]
10.15函数也可以传递、Lambda.mp4 [55M]
10.16修饰.mp4 [50.8M]
10.17List Comprehensions（一）.mp4 [52M]
10.18List Comprehensions（二）.mp4 [44.9M]
10.1答疑—strip的功能.mp4 [33.9M]
10.2List（一）.mp4 [41.7M]
10.3List（二）.mp4 [44.3M]
10.4List（三）.mp4 [26.8M]
10.5Tuple.mp4 [47.2M]
10.6Dictionary（一）.mp4 [38.8M]
10.7答疑回顾.mp4 [29.5M]
10.8Dictionary（二）.mp4 [61M]
10.9Set.mp4 [42M]
11.10Advanced Python（三）.mp4 [73.6M]
11.11Advanced Python（四）.mp4 [82.2M]
11.1Modules，Packages，and Programs（一）.mp4 [61.6M]
11.2Modules，Packages，and Programs（二）.mp4 [73.5M]
11.3Python Standard Library.mp4 [90.9M]
11.4Python System（一）.mp4 [35M]
11.5Python System（二）.mp4 [63.4M]
11.6Python System（三）.mp4 [71.8M]
11.7Python System（四）.mp4 [21.3M]
11.8Advanced Python（一）.mp4 [59.3M]
11.9Advanced Python（二）.mp4 [54.4M]
12.10Crawl—BOSS直聘（四）.mp4 [184.7M]
12.11Crawl—BOSS直聘（五）.mp4 [194.2M]
12.12Crawl—BOSS直聘（六）.mp4 [130.1M]
12.13作业：英雄列表整合（一）.mp4 [119.9M]
12.14作业：英雄列表整合（二）.mp4 [123M]
12.15作业：英雄列表整合（三）.mp4 [192.2M]
12.1计算机网络基础.mp4 [28.9M]
12.2网站.mp4 [26.2M]
12.3示例分析.mp4 [165.1M]
12.4知识回顾及预习.mp4 [74.8M]
12.5HTML，CSS，Browser（一）.mp4 [61.9M]
12.6HTML，CSS，Browser（二）.mp4 [52.2M]
12.7Crawl—BOSS直聘（一）.mp4 [90.9M]
12.8Crawl—BOSS直聘（二）.mp4 [194.6M]
12.9Crawl—BOSS直聘（三）.mp4 [78M]
13.1课程简述及小测试.mp4 [20.6M]
13.2自然科学vs数学.mp4 [14.8M]
13.3随机试验.mp4 [17.9M]
13.4古典概型（一）.mp4 [11.8M]
13.5古典概型（二）.mp4 [24.2M]
13.6条件概率.mp4 [18.1M]
13.7贝叶斯公式（一）.mp4 [18M]
13.8贝叶斯公式（二）.mp4 [18.4M]
13.9独立性.mp4 [21.6M]
14.1随机变量.mp4 [19.7M]
14.20-1分布和伯努利实验（一）.mp4 [14.3M]
14.3伯努利实验例题讲解（一）.mp4 [33.3M]
14.4伯努利实验例题讲解（二）.mp4 [14M]
14.5随机变量分布函数（一）.mp4 [20.8M]
14.6随机变量分布函数（二）.mp4 [19.8M]
14.7随机变量分布函数（三）.mp4 [18.5M]
14.8随机变量分布函数（四）.mp4 [10.2M]
14.9随机变量分布函数（五）.mp4 [21.4M]
15.10正态分布例题讲解（三）.mp4 [12.3M]
15.1分布函数例题及贝叶斯公式回顾.mp4 [21.6M]
15.2贝叶斯公式例题（一）.mp4 [17.8M]
15.3贝叶斯公式例题（二）.mp4 [24.1M]
15.4贝叶斯定理思想归纳.mp4 [16.1M]
15.5贝叶斯定理应用总结及分布函数.mp4 [10.9M]
15.6正态分布例题讲解（一）.mp4 [20.2M]
15.7正态分布例题讲解（二）.mp4 [10.1M]
15.8离散型分布函数.mp4 [8.4M]
15.9连续型分布函数.mp4 [16.4M]
16.1离散型分布函数的数学期望.mp4 [14.8M]
16.2连续型分布函数的数学期望.mp4 [10.6M]
16.3例题讲解（一）.mp4 [19.9M]
16.4例题讲解（二）.mp4 [18.1M]
16.5例题讲解（三）.mp4 [17M]
16.6正态分布的标准差定义.mp4 [13.9M]
16.7数学期望及例题讲解.mp4 [19.8M]
16.8方差及例题讲解.mp4 [15.7M]
17.1二维随机变量（一）.mp4 [21.6M]
17.2二维随机变量（二）.mp4 [22.1M]
17.3二维随机变量（三）.mp4 [21.1M]
17.4N维随机变量（一）.mp4 [10.8M]
17.5N维随机变量（二）.mp4 [17.3M]
17.6中心极限定理（一）.mp4 [16M]
17.7中心极限定理（二）.mp4 [22.1M]
17.8随机样本与箱线图.mp4 [17.2M]
17.9SPSS数据分析.mp4 [65.5M]
18.10单因素方差分析（二）.mp4 [70.8M]
18.11两因素方差分析.mp4 [72.9M]
18.12卡方检验（一）.mp4 [41.1M]
18.13卡方检验（二）.mp4 [29.2M]
18.14卡方检验（三）.mp4 [28.6M]
18.15简单线性回归（一）.mp4 [41.7M]
18.16简单线性回归（二）.mp4 [41.5M]
18.1T检验理论推导和前提.mp4 [13.4M]
18.2单样本t检验（一）.mp4 [38.7M]
18.3单样本t检验（二）.mp4 [62.3M]
18.4独立样本t检验（一）.mp4 [32.5M]
18.5独立样本t检验（二）.mp4 [38.4M]
18.6配对样本t检验（一）.mp4 [37.8M]
18.7配对样本t检验（二）.mp4 [25.7M]
18.8方差分析.mp4 [14.6M]
18.9单因素方差分析（一）.mp4 [45.2M]
19.10统计、排序和存储array.mp4 [40.2M]
19.11Pandas简单介绍和Series.mp4 [49.3M]
19.12Series.mp4 [38.6M]
19.13DataFrame.mp4 [24.9M]
19.14Titanic example.mp4 [36.3M]
19.15Index object、Reindex.mp4 [14.4M]
19.16Drop Data、Slice Data.mp4 [22.9M]
19.17Data Alignment、Rank and Sort.mp4 [26.6M]
19.1NumPy简单介绍.mp4 [131.5M]
19.2创建矩阵（一）.mp4 [39M]
19.3创建矩阵（二）.mp4 [21.9M]
19.4算术操作和矩阵计算.mp4 [22.9M]
19.5Several Useful Operations.mp4 [23.6M]
19.6一维矩阵.mp4 [34.1M]
19.7多维矩阵（一）.mp4 [31.6M]
19.8多维矩阵（二）.mp4 [39.6M]
19.9Generate Grid、NumPy where function.mp4 [21.9M]
20.10Data-ink ratio举例（一）.mp4 [42.7M]
20.11Data-ink ratio举例（二）.mp4 [68.4M]
20.12Seaborn：Regression plot.mp4 [28.4M]
20.13Bar plot、FacetGrid.mp4 [69.3M]
20.14Pair Plot、Joint Plot与Line Plot.mp4 [40.1M]
20.15Plotly（一）.mp4 [55.6M]
20.16Plotly（二）.mp4 [63.6M]
20.1数据可视化引入（一）.mp4 [142.8M]
20.2数据可视化引入（二）.mp4 [73.5M]
20.3什么是Data Visualization.mp4 [35.7M]
20.4Matplotlib简单介绍.mp4 [16.9M]
20.5Data-ink ratio.mp4 [16.8M]
20.6一次性画图和重复性画图的关系.mp4 [28.4M]
20.7Matplotlib及其元素.mp4 [38M]
20.8Mode.mp4 [26.3M]
20.9Basic elements及画图介绍.mp4 [34.7M]
21.10切片器连接多个数据透视表.mp4 [30.1M]
21.11分组.mp4 [42.4M]
21.12设置数值计算方式与自定义计算项.mp4 [39.8M]
21.13例题练习.mp4 [34.3M]
21.1数据透视表课程引入.mp4 [23.5M]
21.2观察数据及创建数据透视表.mp4 [39.3M]
21.3透视表简单练习.mp4 [16.6M]
21.4如何设置数据透视表的格式、风格.mp4 [30.5M]
21.5报表布局、分类汇总、总计.mp4 [32.3M]
21.6排序与筛选（一）.mp4 [37.2M]
21.7排序与筛选（二）.mp4 [17.6M]
21.8刷新、更改数据源.mp4 [24.8M]
21.9切片器操作及简单练习.mp4 [28M]
22.10饼状图答疑.mp4 [6.9M]
22.11练习（一）.mp4 [47M]
22.12练习（二）.mp4 [28.7M]
22.13练习（三）.mp4 [21.5M]
22.14练习（四）.mp4 [28.3M]
22.15练习（五）.mp4 [37.6M]
22.1课前回顾.mp4 [24.2M]
22.2柱状图（一）.mp4 [30.1M]
22.3柱状图（二）.mp4 [36.9M]
22.4柱状图（三）.mp4 [22.7M]
22.5柱状图（四）.mp4 [25.6M]
22.6饼状图、线状图.mp4 [47.5M]
22.7图表结合.mp4 [19.3M]
22.8数据透视图（一）.mp4 [27.7M]
22.9数据透视图（二）.mp4 [24.7M]
23.10创建Dashboard（一）.mp4 [30.6M]
23.11创建Dashboard（二）.mp4 [37.9M]
23.12创建Dashboard（三）.mp4 [24.6M]
23.13课程内容回顾（一）.mp4 [31.8M]
23.14课程内容回顾（二）.mp4 [34.9M]
23.1课前回顾.mp4 [21.8M]
23.2mini图和时间轴.mp4 [32.1M]
23.3数据仪表盘示例及创建仪表盘的步骤.mp4 [27.1M]
23.4创建仪表盘的注意事项与演示.mp4 [17.3M]
23.5创建数据仪表盘：观察整理数据.mp4 [33M]
23.6建立数据透视表和图表（一）.mp4 [24.1M]
23.7建立数据透视表和图表（二）.mp4 [33.9M]
23.8建立数据透视表和图表（三）.mp4 [45.7M]
23.9建立数据透视表和图表（四）.mp4 [41.3M]
24.10金融数据分析与人力资源数据分析.mp4 [44.9M]
24.11医疗健康数据分析.mp4 [35.2M]
24.12供应链数据分析与体育数据分析.mp4 [34.8M]
24.13互联网数据分析.mp4 [20.6M]
24.14数据清理—数据分析前必不可少的步骤.mp4 [22.6M]
24.15Case 1：数据质量控制（一）.mp4 [33.4M]
24.16Case 1：数据质量控制（二）.mp4 [19.7M]
24.17数据分析流程及分类.mp4 [26.5M]
24.18描述性、预测性、指导性数据分析.mp4 [18.1M]
24.19Case 2：如何使用数据解答商业问题.mp4 [45.8M]
24.1商业数据分析的驱动力.mp4 [34.8M]
24.20答疑及大数据简述.mp4 [9.6M]
24.2什么是商业数据分析（一）.mp4 [32.2M]
24.3什么是商业数据分析（二）.mp4 [15.7M]
24.4不同部门的应用场景及流程综述.mp4 [35.6M]
24.5市场推广数据分析（一）.mp4 [18.8M]
24.6市场推广数据分析（二）.mp4 [18.7M]
24.7新业务开发.mp4 [19.2M]
24.8销售管理和其他应用场景.mp4 [38.1M]
24.9不同行业的应用场景及答疑.mp4 [10.6M]
25.10Marketing Analytics Landscape及答疑.mp4 [29.5M]
25.11Marketing Mix Model.mp4 [26.9M]
25.12MMM模型例题分析.mp4 [30.7M]
25.13市场反应度、线性模型及指数模型.mp4 [23.4M]
25.14Contribution与Optimization.mp4 [28.7M]
25.15Digital Marketing.mp4 [24.7M]
25.16Attribution及举例.mp4 [32.2M]
25.17Linear Attribution及两-模型-较分析.mp4 [13.5M]
25.1市场漏斗模型Marketing Funnel（一）.mp4 [37.6M]
25.2市场漏斗模型Marketing Funnel（二）.mp4 [34.2M]
25.3Samples.mp4 [12.8M]
25.4Marketing vs Marketing Analytics（一）.mp4 [14.7M]
25.5Marketing vs Marketing Analytics（二）.mp4 [44.9M]
25.6Marketing Analytics（一）.mp4 [26.7M]
25.7Marketing Analytics（二）.mp4 [32.2M]
25.8Segmentation及举例.mp4 [40.6M]
25.9Acquisition Model与Analytics Cycle.mp4 [22.2M]
26.1ROI—投资回报率.mp4 [46M]
26.2MER—推广成本营收-.mp4 [16.9M]
26.3CAC—顾客获取成本及其他重要指标.mp4 [33.7M]
26.4STP框架.mp4 [21M]
26.5STP举例：地毯纤维.mp4 [14.5M]
26.6市场细分需要收集的数据.mp4 [38.6M]
26.7市场细分的主要步骤及聚类分析举例.mp4 [13.4M]
26.8Case Study：应当选择-个细分市场？.mp4 [28.6M]
26.9目标市场、市场定位及行业前瞻.mp4 [36.9M]
27.10Missing Data与Transformation.mp4 [29.6M]
27.11Web Data Preparation.mp4 [9.4M]
27.12Data Cleaning：Airbnb Listings（一）.mp4 [42.8M]
27.13Data Cleaning：Airbnb Listings（二）.mp4 [42.9M]
27.14Data Cleaning：Airbnb Listings（三）.mp4 [46.6M]
27.1数据处理方法引入.mp4 [23.5M]
27.2Data Source：Excel.mp4 [29.4M]
27.3Data Source：Delimited format与Fixed length.mp4 [46.7M]
27.4Data File与Web Data.mp4 [23.2M]
27.5Data Source：RDBMS.mp4 [44.5M]
27.6Data Types（一）.mp4 [36.5M]
27.7Data Types（二）.mp4 [22.3M]
27.8Missing Data与Data Quality Issues.mp4 [35.8M]
27.9Data Preparation与Data Cleansing.mp4 [28.2M]
28.10Feature Extraction（一）.mp4 [20.6M]
28.11Feature Extraction（二）.mp4 [28.9M]
28.12答疑：Sklearn安装.mp4 [25.1M]
28.13Feature selection.mp4 [20M]
28.14Learning algorithm（一）.mp4 [24.5M]
28.15Learning algorithm（二）.mp4 [26.4M]
28.16Extreme Example.mp4 [29.9M]
28.17Model evaluation&selection及回顾.mp4 [27.8M]
28.1Sklearn介绍.mp4 [40.5M]
28.2什么是机器学习.mp4 [35.9M]
28.3General Learning Models-Supervised（一）.mp4 [30.8M]
28.4General Learning Models-Supervised（二）.mp4 [17.5M]
28.5General Learning Models-Unsupervised.mp4 [8.3M]
28.6Part1.Feature Extraction.mp4 [18M]
28.7Part2.Learning Algorithms.mp4 [17.2M]
28.8Sklearn安装.mp4 [13.6M]
28.9Dataset.mp4 [43.9M]
29.10模型的诊断（二）.mp4 [22.7M]
29.11线性回归分析步骤.mp4 [16.6M]
29.12Python实例：利用数据点建立模型.mp4 [24.1M]
29.13如何求线性方程斜率与截距.mp4 [20.6M]
29.14如何评价模型的好坏.mp4 [11.9M]
29.15Linear Regression on Boston housing dataset.mp4 [38.1M]
29.16Method 1：sklearn package.mp4 [35M]
29.17Method 2：statsmodels package.mp4 [40.6M]
29.1课程引入.mp4 [15.4M]
29.2什么是模型？.mp4 [11M]
29.3什么是回归分析及其分类.mp4 [19.5M]
29.4什么是线性回归？.mp4 [21.4M]
29.5自变量与因变量.mp4 [15.7M]
29.6线性回归模型及所需满足的条件.mp4 [19.4M]
29.7线性回归前提假设.mp4 [38.6M]
29.8残差Residual及系数的估计.mp4 [23.6M]
29.9模型的诊断（一）.mp4 [29.3M]
30.10过拟合vs欠拟合与交叉验证.mp4 [36.7M]
30.11逻辑回归分析流程.mp4 [8.2M]
30.12数据导入.mp4 [26.9M]
30.13Data Exploratory.mp4 [34.7M]
30.14Create dummy variables & Feature Selection.mp4 [28.5M]
30.15Implementing the model & Logistic Regression Model Fitting.mp4 [16.4M]
30.16Cross Validation & Confusion Matrix.mp4 [26M]
30.1课程引入.mp4 [14.5M]
30.2监督式vs非监督式机器学习.mp4 [40.8M]
30.3分类vs聚类.mp4 [35.1M]
30.4分类算法vs回归分析.mp4 [6.3M]
30.5为什么线性模型不适用？.mp4 [17.4M]
30.6逻辑回归的前提假设.mp4 [17.9M]
30.7逻辑回归的公式及问题.mp4 [21.4M]
30.8混淆矩阵与ROC曲线.mp4 [34M]
30.9模型永远都不是完美的.mp4 [10.7M]
31.10Growth hacking-0304.mp4 [283.7M]
31.11Growth hacking-0304.mp4 [8.5M]
31.12MySQL 1 -0305.mp4 [285.2M]
31.13MySQL 1-0305.mp4 [110.9M]
31.14MySQL2 -0306.mp4 [523.9M]
31.15NoSQL Database in Big Data-0307.mp4 [365.4M]
31.16Power BI-0307.mp4 [71.6M]
31.17E-Commerce-0309.mp4 [360.2M]
31.18E-Commerce-0310.mp4 [270.3M]
31.19Gaming Analytics-0312.mp4 [307.9M]
31.1Classification & Clustering Classification vs. Clustering-0223.mp4 [356.1M]
31.20Gaming Analytics-0314.mp4 [310.8M]
31.21感官分析1-0316.mp4 [293.3M]
31.22感官分析2-0316.mp4 [322.8M]
31.23感官分析3-0317.mp4 [329.3M]
31.24感官分析4-0317.mp4 [294.6M]
31.25A-B Testing-0319.mp4 [343.9M]
31.26A-B Testing-0320.mp4 [368.9M]
31.27Capstone-0323.mp4 [459.7M]
31.28Capstone-0324.mp4 [653.7M]
31.2顾客体验Customer Experience-0224.mp4 [271.3M]
31.3定价Pricing-0225.mp4 [268.4M]
31.4SPSS与问卷分析-0226.mp4 [492.7M]
31.5市场研究的基础知识-0227.mp4 [302M]
31.6市场营销的研究应用-0228.mp4 [258.3M]
31.7CRM & RFM- 0301.mp4 [332.3M]
31.8CRM & RFM -0302.mp4 [390.9M]
31.9新业务开发及销售运营管理-0303.mp4 [294.1M]
课件.rar [915.7M]