GRIFFIN CHOW
SUMMARY
拥有两年后端开发经验,擅长使用Golang(GoFrame框架)进行敏捷开发与业务迭代;同时具备基本的数据分析能力,能够独立完成从数据采集、清洗到可视化呈现的全流程工作。在实际项目中积累了丰富的AB测试经验,深度参与过从实验设计、数据埋点到统计分析的完整闭环,对数据驱动决策有深刻理解。
技术栈方面,熟练掌握SQL进行复杂查询与数据处理,熟练使用Python(Pandas等)进行数据分析与爬虫开发,能够构建自动化脚本提升工作效率;熟练使用Excel进行数据透视、函数处理以及图表可视化;擅长运用Excel、ECharts、Tableau等工具进行数据可视化,将复杂数据转化为清晰易懂的视觉呈现。此外,紧跟AI技术前沿,具备Vibe Coding实践经验,能够使用Coze平台构建智能体应用,了解LLM、RAG、MCP等大模型核心概念及常见机器学习算法。
善于将后端开发思维与数据分析能力相结合,既能实现业务需求的快速迭代,也能通过数据洞察驱动产品优化。具备良好的数据敏感度,能够从业务数据中快速发现异常与机会点;拥有较强的学习能力,能够快速掌握新技术并应用到实际项目中;注重团队协作与跨部门沟通,善于将技术方案转化为易于理解的业务语言,推动项目落地,为业务增长提供技术支撑与数据决策依据。
SKILLS
数据分析
- 熟练使用SQL进行数据查询与处理,包括多表联查、窗口函数、CTE、子查询等复杂查询
- 熟练使用Python进行数据分析,掌握Pandas、NumPy等核心库进行数据清洗、处理和分析
- 熟练使用Excel进行数据处理,包括常用函数(SUMIFS、COUNTIFS、XLOOKUP、INDEX/MATCH等)、数据透视表、条件格式等
- 熟悉AB测试完整流程,包括实验设计、样本量计算、数据埋点、假设检验、效应量评估等
- 掌握RFM模型、用户行为分析、漏斗分析等常见数据分析方法
- 了解K-Means等常见聚类算法和机器学习基础概念
数据可视化
- 熟练使用ECharts进行交互式图表开发,能够制作雷达图、时间轴、词云等复杂可视化
- 熟练使用Tableau进行BI报表制作和数据看板搭建
- 掌握Excel图表制作,能够设计清晰美观的数据报表
数据采集与自动化
- 熟练使用Python构建网络爬虫(Selenium、Requests等),进行数据采集
- 能够编写Python自动化脚本,提升数据处理效率
- 了解数据清洗、数据验证的常见方法和最佳实践
AI与智能体开发
- 具备Vibe Coding实践经验,能够高效利用AI辅助编程
- 熟练使用Coze平台构建智能体应用,具备Prompt工程能力
- 了解LLM(大语言模型)、RAG(检索增强生成)、MCP(模型上下文协议)等前沿技术概念
- 了解常见机器学习算法原理和应用场景
{
"tooltip": {
"trigger": "item"
},
"radar": {
"radius": "65%",
"indicator": [
{ "name": "SQL", "max": 10 },
{ "name": "Excel", "max": 10 },
{ "name": "Python", "max": 10 },
{ "name": "业务分析", "max": 10 },
{ "name": "可视化", "max": 10 },
{ "name": "AI", "max": 10 }
],
"splitNumber": 11,
"axisLine": {
"lineStyle": { "color": "#999" }
},
"splitLine": {
"lineStyle": { "color": "#ccc" }
},
"splitArea": {
"show": false
},
"name": {
"fontSize": 12
}
},
"series": [
{
"type": "radar",
"data": [
{
"value": [8, 7, 7, 7, 8, 9],
"name": "个人技能",
"areaStyle": {
"color": "rgba(60,150,250,0.25)"
},
"lineStyle": {
"color": "#3c96fa",
"width": 2
},
"symbol": "circle",
"symbolSize": 5,
"itemStyle": {
"color": "#3c96fa"
},
"label": {
"show": false
}
}
]
}
]
}
EXPERIENCE
{
"grid": { "left": 60, "right": 40, "top": 70, "bottom": 70 },
"legend": {
"data": ["大学", "考研", "工作"],
"left": "center",
"top": 40,
"icon": "roundRect",
"itemWidth": 18,
"itemHeight": 12,
"textStyle": { "fontSize": 12 }
},
"xAxis": {
"type": "time",
"name": "时间",
"nameLocation": "end",
"nameGap": 10,
"min": "2019-09-01",
"max": "2025-10-30",
"axisLine": { "lineStyle": { "color": "#666" } },
"axisTick": { "alignWithLabel": true },
"axisLabel": { "fontSize": 11, "hideOverlap": true },
"splitLine": {
"show": true,
"lineStyle": { "type": "dashed", "color": "#e0e0e0" }
}
},
"yAxis": {
"type": "value","name": "活跃度",
"nameLocation": "end",
"nameGap": 10,
"min": 0,
"max": 100,
"axisLabel": { "show": false },
"axisTick": { "show": false },
"axisLine": { "show": false },
"splitLine": {
"show": true,
"lineStyle": { "type": "dashed", "color": "#efefef" }
}
},
"dataZoom": [
{
"type": "slider",
"xAxisIndex": 0,
"height": 24,
"bottom": 28,
"filterMode": "none",
"fillerColor": "rgba(70,130,255,0.15)",
"handleSize": "80%",
"startValue": "2019-09-01",
"endValue": "2025-10-30"
},
{ "type": "inside", "xAxisIndex": 0, "filterMode": "none" }
],
"tooltip": {
"trigger": "axis",
"axisPointer": { "type": "line" }
},
"color": ["#3C78FF", "#FF9F3C", "#33C58D"],
"series": [
{
"name": "大学",
"type": "line",
"smooth": true,
"showSymbol": false,
"lineStyle": { "width": 2 },
"areaStyle": {
"color": {
"type": "linear",
"x": 0, "y": 0, "x2": 0, "y2": 1,
"colorStops": [
{ "offset": 0, "color": "rgba(60,120,255,0.45)" },
{ "offset": 1, "color": "rgba(60,120,255,0.05)" }
]
}
},
"data": [
["2019-09-01", 0],
["2020-02-01", 12],
["2020-05-04", 30],
["2020-12-31", 35],
["2021-06-30", 40],
["2022-04-22", 80],
["2022-05-27", 60],
["2022-09-30", 65],
["2023-03-04", 50],
["2023-05-01", 30],
["2023-06-30", 0]
],
"markPoint": {
"symbol": "circle",
"symbolSize": 10,
"label": { "show": false },
"emphasis": {
"label": {
"show": true,
"color": "#000",
"backgroundColor": "rgba(255,255,255,0.85)",
"borderColor": "#3C78FF",
"borderWidth": 1,
"padding": [4,6],
"fontSize": 11,
"formatter": "{b}"
}
},
"itemStyle": {
"color": "#3C78FF",
"shadowBlur": 6,
"shadowColor": "rgba(0,0,0,0.15)"
},
"data": [
{ "name": "个人博客", "coord": ["2020-05-04", 30], "value": 30 },
{ "name": "Kaggel 银", "coord": ["2022-04-19", 80], "value": 80 },
{ "name": "蓝桥杯Python组三等奖", "coord": ["2022-05-27", 60], "value": 60 },
{ "name": "1+X中级人工智能数据处理师", "coord": ["2023-03-04", 50], "value": 50 }
]
}
},
{
"name": "考研",
"type": "line",
"smooth": true,
"showSymbol": false,
"lineStyle": { "width": 2 },
"areaStyle": {
"color": {
"type": "linear",
"x": 0, "y": 0, "x2": 0, "y2": 1,
"colorStops": [
{ "offset": 0, "color": "rgba(255,159,60,0.45)" },
{ "offset": 1, "color": "rgba(255,159,60,0.05)" }
]
}
},
"data": [
["2022-01-21", 0],
["2022-03-15", 25],
["2022-06-15", 45],
["2022-09-15", 55],
["2022-12-25", 60],
["2023-06-30", null],
["2023-12-31", null],
["2024-01-21", 0],
["2024-04-01", 40],
["2024-07-01", 55],
["2024-10-01", 65],
["2024-12-25", 70]
],
"markPoint": {
"symbol": "circle",
"symbolSize": 10,
"label": { "show": false },
"emphasis": {
"label": {
"show": true,
"color": "#000",
"backgroundColor": "rgba(255,255,255,0.85)",
"borderColor": "#FF9F3C",
"borderWidth": 1,
"padding": [4,6],
"fontSize": 11,
"formatter": "{b}"
}
},
"itemStyle": {
"color": "#FF9F3C",
"shadowBlur": 6,
"shadowColor": "rgba(0,0,0,0.15)"
},
"data": [
{ "name": "分数290", "coord": ["2022-12-25", 60], "value": 60 },
{ "name": "分数328", "coord": ["2024-12-25", 70], "value": 70 }
]
}
},
{
"name": "工作",
"type": "line",
"smooth": true,
"showSymbol": false,
"lineStyle": { "width": 2 },
"areaStyle": {
"color": {
"type": "linear",
"x": 0, "y": 0, "x2": 0, "y2": 1,
"colorStops": [
{ "offset": 0, "color": "rgba(51,197,141,0.45)" },
{ "offset": 1, "color": "rgba(51,197,141,0.05)" }
]
}
},
"data": [
["2023-03-21", 0],
["2023-04-22", 50],
["2023-06-01", 65],
["2023-07-21", 80],
["2023-12-31", 78],
["2024-06-30", 75],
["2024-12-31", 73],
["2025-06-30", 55],
["2025-09-27", 30],
["2025-10-10", 40],
["2025-11-03", 55]
],
"markPoint": {
"symbol": "circle",
"symbolSize": 10,
"label": { "show": false },
"emphasis": {
"label": {
"show": true,
"color": "#000",
"backgroundColor": "rgba(255,255,255,0.85)",
"borderColor": "#33C58D",
"borderWidth": 1,
"padding": [4,6],
"fontSize": 11,
"formatter": "{b}"
}
},
"itemStyle": {
"color": "#33C58D",
"shadowBlur": 6,
"shadowColor": "rgba(0,0,0,0.15)"
},
"data": [
{ "name": "数据实习", "coord": ["2023-03-21", 0], "value": 0 },
{ "name": "后端实习", "coord": ["2023-04-22", 50], "value": 50 },
{ "name": "后端开发", "coord": ["2023-07-21", 80], "value": 80 },
{ "name": "辞职学习&接单", "coord": ["2025-09-27", 30], "value": 30 }
]
}
}
]
}
PROJECTS
数据分析
基于STEAM平台游戏黑神话·悟空的评论分析 steam
基于Steam平台3626条《黑神话·悟空》简体中文评论数据,通过数据清洗、探索性分析、情感分析和可视化展示,深度剖析游戏口碑;推荐率71.15%,正面情感58.88%,平均游戏时长54小时,揭示玩家对剧情、战斗系统、文化元素的关注热点
电商用户行为分析与聚类 kaggle
基于1000条电商用户数据,运用RFM模型和K-Means聚类算法进行用户价值分群;深度剖析用户画像、活跃度和消费行为,为不同用户群体制定差异化营销策略,助力精准运营
Web页面新功能用户点击率AB-test数据分析 公司项目脱敏
基于12万条AB测试数据,运用Mann-Whitney U检验分析Web新功能点击率提升效果;实验组点击率3.86%显著高于对照组3.47%(p<0.05),提升11.52%;涵盖完整AB测试流程:改动确定、核心指标确定、样本量计算、实验周期确定、分流与随机化、数据埋点设计、灰度测试、假设检验、效应量评估及多维度可视化分析
基于2025年10月的鱼泡直聘数据分析岗的分析与可视化 刚做的 🥵
基于鱼泡直聘284条数据分析岗位数据,深度剖析薪资水平(平均14564元/月)、经验要求、学历分布、技能需求(SQL、Python核心)及地域差异,揭示一线城市与新一线城市的就业机会与薪资结构
智能体
后端开发
TODO…
EDUCATION
本科