📊 KPI Data Dashboard with Google Sheets 🔎 Google Sheets Formulas Pivot Tables
Problem:
Fragmentation: Critical hotel metrics—real-time occupancy, projected arrivals, out-of-order rooms, and variable pricing—were siloed across separate, numbers-only tables.
Blind Spots: Lack of a unified, real-time visual overview risked missed opportunities for optimizing room pricing and resource allocation.
Solution:
Automation: Engineered an automated Google Sheets dashboard to parse, aggregate, and visualize dynamic time-series data, providing management with real-time operational health and proactive pricing insights.
Experience the Live Dashboard!
Daily KPIs by Hour
🧬 Experimental Groups Data Analysis & Stats 🔎 PySimpleGUI Pandas Matplotlib Numpy Scipy Math
Problem:
Bottleneck: Lab experiments generated multi-group behavioral datasets requiring tedious, manual reformatting before moving to statistical software.
Risk: High susceptibility to human error during manual data manipulation across extensive trial runs.
Visual Gap: Default statistical software produced rigid, aesthetically unappealing charts that lacked publication-ready quality.
Solution:
Automation: Engineered a custom PySimpleGUI desktop application to automate ingestion, group-by-group statistical analysis, and high-fidelity visualization in seconds.
PySimpleGUI
Graphs generated with SEM & ANOVA tables
Short video demonstrating the desktop application's functionality
🧩 Experimental Design using Least Different Group Matching 🔎 PySimpleGUI Pandas Itertools Matplotlib re
Problem:
Assignment Complexity: Assigning pair-housed subjects to treatment versus control groups based on baseline data required minimizing initial differences while strictly preserving cage-mate pairs.
Scale: Evaluating thousands of potential group assignment combinations to find the absolute minimum variance was entirely impractical to perform manually.
Solution:
Algorithm & Interface: Developed a PySimpleGUI desktop app leveraging Pandas and Itertools to systematically compute all group permutations, identify optimal minimum-variance pairings, and generate Matplotlib visual verification.
PySimpleGUI
Graph generated showing hypothetical two-group assignments that are the least different between each other out of all possible group assignments
Short video demonstrating the desktop application's functionality
💸 Robinhood Portfolio Insights 🔎 Robin_Stocks API Pandas Numpy Matplotlib
Problem:
Evaluation Gap: Tracking individual stock portfolio performance over time lacked a systematic, automated method for historical benchmarking.
Solution:
API Pipeline:
Built a Python automation script using the robin_stocks API
to log daily positions into CSV, leveraging Pandas and Matplotlib
to benchmark portfolio total return and asset allocation against
diversified ETFs.
Script created using the Robin_Stocks Python library and the Robinhood API
- Top line graph displaying dollar value change over time
- Line graph displaying percent change over time
- Bar graph displaying total change currently
- Pie chart displaying asset class's portfolio distribution
- Pie chart displaying each asset class's current return on investment
🐁 Wheel Running Data Parsing & Automation 🔎 PySimpleGUI Openpyxl Matplotlib
Problem:
Data Deluge: Running-wheel experiments generated millions of timestamped revolution data points across multiple subjects and extended cycles.
Filtering Bottleneck: Manually filtering valid running bouts (>=3 RPM) and isolating activity between light (inactive) and dark (active) phases was labor-intensive and error-prone.
Solution:
Parsing Engine: Created a PySimpleGUI and Openpyxl desktop application to ingest, filter, and aggregate multi-subject running data by phase in seconds, complete with automated Matplotlib comparative graphs.
PySimpleGUI
Graph generated showing mean values of different wheel running behaviors during the animal's active 12hr light-cycle versus their inactive 12hr light-cycle.
Short video demonstrating the desktop application's functionality
💰 Visual Insights from Financial Data 🔎 Pandas Numpy Matplotlib
Problem:
Category Inaccuracies: Commercial bank and credit card dashboards relied on broad, rigid spending categories that frequently miscategorized transactions and lacked trend tracking.
Solution:
Custom Pipeline: Developed a Pandas-powered Python script to clean and re-categorize statement CSVs, generating granular Matplotlib stacked bar charts for precise monthly expenditure tracking.
Checking account activity divided into categories by month
Credit Card expenditures divided into categories by month
📄 Text-to-Excel Automation for Activity Data 🔎 Pandas Xlwt Matplotlib re
Problem:
Extraneous Data: Infrared beam-break chambers outputted massive, unstructured text files containing substantial superfluous data alongside target locomotion metrics.
Manual Friction: Manually extracting and formatting relevant time-bins for multiple subjects across multi-hour trials was painfully slow and prone to transcription errors.
Solution:
Text Parser: Built a PySimpleGUI desktop utility using regular expressions and Pandas to parse raw text outputs, normalize data into clean Excel spreadsheets, and auto-generate exploratory time-bin graphs.
PySimpleGUI
Graph generated showing mean values of all subjects locomotion for each 5min time period over the 1hr trial period along with SEM of each time bin. Graph is charactoristic of the inital high exploritory behavior of a novel environment that tapers off over time.
Short video demonstrating the desktop application's functionality
Click Link to Try My App: PySimpleGUI Emulated on Streamlit
Streamlit Emulation Preview Image