You will get Inventory Management CSV to Excel (XLSX), Data Entry, Data Cleaning Report
Project details
I deliver professional, accurate, and easy-to-use Excel solutions tailored to your business needs. Whether you need an inventory tracker, product catalog, CSV to Excel (XLSX) conversion, data cleaning, formatting, formulas, or dashboards, I focus on quality, efficiency, and attention to detail. Every workbook is well-organized, scalable, and designed to save time, reduce errors, and simplify data management.
Database Type
MS SQL, SQLiteWhat's included
| Service Tiers |
Starter
$20
|
Standard
$45
|
Advanced
$95
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Source Code | - | - | - |
About Innocent
Data Analyst | SQL, Python, Excel | Data Cleaning | Business Insight
Kampala, Uganda - 6:33 am local time
exports dashboards nobody believes. “I'll handle that.”
I turn dirty, unorganized data collections into clean, ready to use
information faster and more accurately.”
What I give you:
Data Cleaning & ETL: Raw data in. From reliable databases.
→ Advanced SQL: CTEs, window functions, queries for precision warehouse.
→ Python Automation: Scripts to avoid manual repetitive work.
→ Excel Dashboards: Dashboards used in meetings, not just reports.
I provide no pretty files. I provide files that answer the
business that your company is already asking.
Message me – and I’ll put your data to work for you.
Steps for completing your project
After purchasing the project, send requirements so Innocent can start the project.
Delivery time starts when Innocent receives requirements from you.
Innocent works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Discovery
I will review your raw dataset and project requirements to map out the best formula structures (XLOOKUP, FILTER) and visualization layouts for your specific goals.
Data Cleaning & Power Query Processing
I will import your data into Power Query to eliminate duplicate records, format inconsistent dates, fix structural errors, and build a clean data model