面向调研数据、业务数据
和结构化表格,
提供数据整理、统计分析、代码处理
与图表报告。
先评估数据结构和处理目标,
再确认服务范围与交付文件。
For survey data, business datasets,
and structured spreadsheets,
we handle data cleaning, statistical analysis,
code processing, and chart reporting.
Scope is reviewed before work starts,
so files and deliverables stay clear.
适合不确定具体方法,需要先看数据再定范围的综合需求。For broad requests that need data review before scope is fixed.
适合问卷导出表、调研数据、频率统计、交叉表和图表整理。For survey exports, frequency tables, cross tabs, and charts.
适合描述统计、差异比较、相关分析、回归分析和统计表格。For descriptive stats, comparison, correlation, regression, and tables.
适合脚本整理、报错排查、批量数据处理和图表自动导出。For scripts, debugging, batch processing, and chart export.
适合表格清洗、公式整理、数据透视、汇总表和可视化图表。For cleaning, formulas, pivots, summary tables, and visuals.
适合已有数据文件,但还不确定要做清洗、统计、建模还是出图的需求。先看数据样例和处理目标,再把服务范围、交付文件和时间安排说清楚。For datasets where the exact method is not fixed yet. We review sample data and goals first, then confirm scope, deliverables, and timing.
适合问卷星、金数据、Excel 导出表等调研数据。可做字段整理、变量编码、描述统计、频率分布、交叉表和基础图表,交付结果表和说明文件。For survey exports from common form tools and spreadsheets. We handle field mapping, coding, frequency tables, cross tabs, and basic charts.
适合已经有较清晰统计目标的需求,例如描述统计、差异比较、相关分析、回归分析、结果表格整理。结果按实际数据呈现,不承诺特定统计结论。For clearly scoped statistical work: descriptive stats, comparison, correlation, regression, and table formatting. Results follow the data.
适合已有脚本需要整理、报错排查、批量处理数据、自动导出表格或图表的场景。可以按现有数据和目标文件重整处理流程。For scripts that need cleanup, debugging, batch data processing, or automated table and chart export.
适合表格清洗、字段拆分合并、函数公式、数据透视、汇总表和基础图表。交付前会确认表结构、字段含义和最终展示格式。For spreadsheet cleaning, field splitting or merging, formulas, pivots, summary tables, and basic charts.
发送数据样例与处理目标,由团队先评估可行性、工作范围和交付文件Share sample data and goals. We review feasibility, scope, and deliverables first.
→确认服务内容、交付格式、时间安排和调整范围后开始处理Work starts after scope, file format, schedule, and adjustment range are confirmed.
→按确认范围整理数据、生成统计表格、图表和处理说明We prepare data, statistical tables, charts, and processing notes within the agreed scope.
→交付完整文件,按确认范围处理必要调整,文件和步骤可核对Final files are delivered with reviewable steps and agreed-scope adjustments.
适合数据量较小、目标明确的清洗、汇总和基础图表需求。For small datasets with clear cleaning, summary, and basic chart needs.
适合需要统计分析、代码处理、图表报告和结果说明的常规需求。For common analysis needs involving statistics, code processing, charts, and notes.
适合多表数据、复杂清洗、自动化脚本、特殊图表或分阶段交付。For multi-table data, complex cleaning, automation scripts, special charts, or staged delivery.
所有数据本地处理,不上传任何第三方云端。项目完成后按客户要求删除原始数据。All data is processed locally. No third-party cloud uploads. Raw data deleted upon project completion per your request.
按数据量和服务范围评估。常规整理、统计表格和图表说明通常 1-3 天；复杂数据另行确认。Timing depends on data size and scope. Routine cleaning, statistical tables, and chart notes usually take 1-3 days; complex datasets are confirmed separately.
结果按数据实际呈现,包含必要的表格、图表和处理说明。开始前会先确认分析目标和交付格式。Results are presented according to the data, with tables, charts, and processing notes. Goals and formats are confirmed first.
可以按已确认范围处理必要调整。如果新增数据、改目标或增加交付内容,会重新确认范围和时间。Agreed-scope adjustments are supported. New data, new goals, or extra deliverables require scope confirmation.
先确认数据情况、服务范围、交付文件和时间安排,再按双方确认的规则执行。Data condition, service scope, deliverables, and timing are confirmed before work begins.
Python、R、MATLAB、SPSS、Stata、Excel 等工具按需求选用。需要代码文件时,可与结果一起交付。Python, R, MATLAB, SPSS, Stata, Excel, and related tools are selected per project. Code files can be delivered when included in scope.
开始前先看数据结构、字段含义和处理目标,能做什么、交付什么、怎么验收都提前说清楚。处理过程中保留必要步骤记录,方便复核文件和结果。
Before work starts, we review data structure, field meaning, and goals. Scope, deliverables, and review points are clarified upfront, with key processing steps retained for checking.
数研社 (Data Analytics Studio) 提供数据整理、统计分析、代码处理、图表报告和处理说明服务。交付内容以双方确认的服务范围和文件清单为准。
Data Analytics Studio (DAS) provides data preparation, statistical analysis, code processing, chart reporting, and processing notes. Deliverables follow the confirmed scope and file list.
客户应自行确保:
Clients are responsible for ensuring:
我们会谨慎评估:数据来源不清、授权不明或要求误导性展示的项目。
We review carefully: unclear data sources, missing authorization, or requests for misleading presentation.
