Hello, I'm Rei-Taylor
Data Analyst

I transform complex data into actionable insights that drive business decisions. Specializing in statistical analysis, data visualization, and predictive modeling to solve real-world business challenges.

My Expertise

Data Cleaning & Preparation

Expert in transforming raw, messy data into clean, structured datasets using Python (Pandas), SQL, and Excel. Specialized in handling missing data, outliers, and data normalization.

Statistical Analysis

Applying statistical methods including hypothesis testing, regression analysis, and time series forecasting to extract meaningful insights from data.

Data Visualization

Creating compelling, interactive visualizations with Tableau, Power BI, and Python libraries (Matplotlib, Seaborn, Plotly) to communicate insights effectively.

Predictive Modeling

Building machine learning models for classification, regression, and clustering using Scikit-learn, TensorFlow, and statistical modeling techniques.

SQL & Database Management

Writing complex queries, optimizing database performance, and designing efficient data models for relational (MySQL, PostgreSQL) and NoSQL databases.

Business Intelligence

Developing comprehensive dashboards and reports that translate data into actionable business insights for stakeholders at all levels.

Technical Proficiency

I leverage cutting-edge data analysis tools to transform raw data into actionable business insights. Below are my core competencies with industry-standard tools and libraries.

95%
Proficiency

Plotly Dash

Extensive experience building interactive dashboards and web applications. Developed 15+ production dashboards for clients across finance, healthcare, and e-commerce sectors.

90%
Proficiency

Jupyter Notebooks

Daily use for exploratory data analysis, model development, and creating shareable analytical reports. Expert in notebook organization and best practices.

85%
Proficiency

Polars

Leveraging Polars for high-performance data processing with large datasets. 5-10x faster processing compared to Pandas for many operations in production environments.

92%
Proficiency

Pandas

Deep expertise in data manipulation, cleaning, and transformation. Created reusable Pandas functions that reduced data preparation time by 40% for team projects.

88%
Proficiency

SQL

Advanced query writing, optimization, and database design. Worked with PostgreSQL, MySQL, and BigQuery to extract insights from terabyte-scale datasets.

82%
Proficiency

scikit-learn

Building and deploying machine learning models for classification, regression, and clustering. Implemented models that improved prediction accuracy by 25% in client projects.

Featured Projects

Retail Sales Forecasting

Developed a time series forecasting model to predict weekly sales for a major retail chain, improving inventory management and reducing stockouts by 22%.

Python Prophet Tableau SQL
View Project

Customer Segmentation Analysis

Performed clustering analysis on 500K+ customer records to identify high-value segments, resulting in a 15% increase in marketing campaign ROI through targeted messaging.

Python K-Means RFM Analysis Tableau
View Project

Healthcare Patient Outcomes

Analyzed EHR data from 10K+ patients to identify factors influencing treatment success rates, leading to protocol changes that improved patient outcomes by 18%.

Python Logistic Regression SHAP Values Power BI
View Project

Get In Touch

Let's analyze your data!

I'm currently available for freelance projects, consulting, but I'm afraid not full-time because I'm currently working for a local warehouse company. Reach out to discuss how data analysis can help solve your business challenges.

Location

Asia

Phone

+1 ********

Project Details

Jan Feb Mar Apr May
Python Prophet Tableau SQL

This retail sales forecasting project was conducted for a major national retailer with over 500 stores. The goal was to improve inventory management and reduce both stockouts and overstock situations through accurate weekly sales predictions.

22%
Reduction in stockouts
17%
Decrease in excess inventory
92%
Forecast accuracy

Methodology & Approach

1
Data collection and cleaning of 3 years of historical sales data across all product categories and store locations
2
Feature engineering including seasonality indicators, holiday effects, promotional calendars, and external factors like weather data
3
Time series decomposition to identify trend, seasonal patterns, and residual components across different product categories
4
Implementation of Prophet forecasting model with custom seasonality and holiday effects, validated through backtesting
5
Development of interactive Tableau dashboards for store managers to visualize forecasts and adjust for local events
View Dashboard Demo