STRUCTURE & FORMATS
Consistent fields and values
Standardize column structures, data types, naming conventions, dates, categories and other inconsistent formats.
Data Cleaning & Quality.
I transform messy, inconsistent datasets into structured information that is reliable enough for analysis, reporting or modelling.
Python
SQL
Excel
Data Validation
Data Restructuring
Missing Data
Anomaly Detection
THE PROBLEM
Data can exist without being reliable enough to use.
Real-world datasets often contain missing values, inconsistent formats, invalid records and conflicting information. I build tailored data-cleaning and validation workflows to identify these issues, improve consistency and turn imperfect inputs into data that can be used with greater confidence.
WHAT I CAN IMPROVE
From inconsistent inputs to structured, analysis-ready data.
MISSING & INVALID DATA
Identify gaps before they become errors
Detect missing, impossible or malformed values and determine how they should be handled within the dataset.
CONSISTENCY & DUPLICATES
Remove avoidable contradictions
Identify exact duplicates, conflicting records and inconsistencies across fields that reduce the reliability of the data.
MULTI-SOURCE DATA
Make different datasets work together
Align schemas, field definitions and formats across files or sources so they can be consolidated into one consistent structure.
HOW I WORK
From data-quality diagnosis to a dataset you can trust.
01
DIAGNOSE THE DATA
Find where quality breaks down.
Inspect the structure, completeness, formats and internal consistency of the dataset to identify the issues that matter.
02
CLEAN & STANDARDIZE
Apply consistent rules across the data.
Restructure fields, standardize formats and categories, correct invalid values and address recurring quality problems.
03
VALIDATE & CHECK
Make sure the cleaning actually worked.
Run validation rules and consistency checks to identify remaining anomalies, unexpected values or structural problems.
04
DELIVER & DOCUMENT
Leave the data easier to work with.
Deliver the cleaned dataset together with reusable processing logic or clearly documented transformations where appropriate.
TYPICAL PROJECTS
When messy data gets in the way of the work you actually want to do.
01
SPREADSHEET & DATASET CLEANUP
Turn an inconsistent dataset into a reliable working file.
Clean and restructure spreadsheets or exported datasets containing inconsistent fields, formats, categories or missing values.
02
RECURRING DATA QUALITY CHECKS
Catch quality problems before they propagate.
Build repeatable checks for missing fields, unexpected values, invalid formats and other recurring issues in datasets that are regularly updated.
03
MULTI-SOURCE CONSOLIDATION
Make datasets from different sources compatible.
Standardize schemas, naming conventions and field formats before combining multiple files or feeds into one consistent dataset.
04
DATA PREPARATION FOR ANALYSIS
Get the dataset ready before modelling or reporting begins.
Prepare raw information for statistical analysis, dashboards or machine-learning workflows by improving structure, consistency and completeness.
HAVE A DATA PROBLEM?
Let’s figure out what can be done with it.
Tell me what you are trying to collect, clean, understand or automate. I can help turn it into a concrete analytical project.