You will get I will clean, analyze, and validate your data using Python
Project details
You will get accurate, reproducible, and client-ready data analysis using Python. I can clean messy datasets, detect anomalies, calculate KPIs, perform statistical analysis, and validate key results before delivery. My background in mathematics, statistics, and AI evaluation helps me approach every project with strong quantitative reasoning and careful quality checks. I focus on delivering clear, reliable results you can confidently use.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$39
|
Standard
$89
|
Advanced
$179
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 2 |
Number of Pages Mined/Scraped | 39 | 89 | 179 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$15 - $40
Additional Revision
+$10Frequently asked questions
About Derek
AI Evaluator & Data Analyst | Python, Statistics & Modeling
Shanghai, China - 5:16 pm local time
• AI Evaluation: Hands-on experience reviewing AI-generated code, execution results, edge cases, and model outputs through Centific / OneForma
• Data Analysis: Built a Python-based retail analytics workflow using 1M+ transaction records, covering data cleaning, KPI analysis, reconciliation, and validation
• Technical Skills: Python, Pandas, NumPy, SQL, Excel, statistics, and mathematical modeling
• Bilingual EN/CN: IELTS 6.0 and CET-6, with experience handling English technical materials and bilingual tasks
I focus on accurate, reproducible, and evidence-based results and can support projects involving AI evaluation, data analysis, statistical modeling, and quantitative research.
Steps for completing your project
After purchasing the project, send requirements so Derek can start the project.
Delivery time starts when Derek receives requirements from you.
Derek works on your project following the steps below.
Revisions may occur after the delivery date.
Review Data & Requirements
I review the dataset, clarify the analysis goal, inspect the file structure, and identify missing values, duplicates, invalid records, and other data-quality issues.
Clean & Validate the Data
I apply documented cleaning rules, handle anomalies carefully, and validate the processed data to ensure the analysis is based on consistent and reliable records.