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：
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]
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