Tasha Gill
Data Analyst | Delivering Actionable Insights Through Data Analysis & Visualization
Other links:
About Me
Data Analyst with hands-on experience in exploratory data analysis, cleaning, visualization, and storytelling using SQL, Python, R, and BI tools like Tableau and Power BI.
My journey started in 2021 with the Google Data Analytics Professional Certificate, where I built foundational skills in data processing, analysis, and presentation. I've continued growing through certifications in SQL for Data Science, Data Analysis with Python (freeCodeCamp), and a Udacity Data Analyst Nanodegree (completed 2024). In early 2026, I actively participated in MLZoomCamp2025 to strengthen predictive modeling capabilities.
I'm passionate about using data to uncover trends, identify opportunities, and support better decision-making. Open to roles where analytical skills can contribute to meaningful outcomes.
Skills
Analytical & Business-Relevant Skills
- Exploratory Data Analysis
- Data Cleaning & Wrangling
- Data Visualization & Storytelling
- Data-Driven Insights & Recommendations
- Process & Trend Identification
- Problem Solving
- Communication & Stakeholder Focus
- Data Ethics
Technical Tools
- SQL
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- R (tidyverse, dplyr)
- Tableau, Power BI
- Google BigQuery, Jupyter Notebooks, RStudio
- Microsoft Excel (Advanced)
- Project Management: Asana, Trello
Certifications (Links below)
- Data Analyst Nanodegree, Udacity (November 2024, Credential ID: 141b9374-f5c4-11ee-bbfe-2bc8e12468ce)
- Google Data Analytics Specialization, Coursera (July 2021, Credential ID: ZU5PASHXS62P)
- SQL for Data Science, Coursera (August 2021, Credential ID: YAMKW2CBWG6J)
- Data Analysis with Python, freeCodeCamp (September 2021)
- Intro to Machine Learning, Kaggle (August 2021)
Get in Touch - Email Contact Form
Feel free to reach out about data opportunities or collaborations!
Project Highlights
MLZoomCamp2025: Hardware-Based Steam Games Recommender (2026)
A machine learning-powered web app that recommends Steam games based on your PC hardware, helping users quickly determine if their computer can run specific games, without invasive scans, downloads, or extensive research across the Steam Store.
The project addresses real-world challenges like hardware pricing crises, inconsistent performance data, and time-consuming compatibility checks by combining Steam game data, hardware benchmarks, fuzzy matching, and a trained Random Forest model to predict compatibility.
Key Skills Demonstrated
- Data cleaning and processing of inconsistent datasets with Pandas
- Feature engineering including fuzzy matching and scoring
- Machine learning modeling (Random Forest classification)
- Synthetic data generation for balanced training
- End-to-end ML pipeline development and deployment
- Interactive web app creation with Streamlit
- Model management and version-controlled deployment
- Transforming complex data into user-friendly recommendations
Live Demo: Try the Recommender App
Repository: View Full Project on GitHub
FreeCodeCamp Data Analysis with Python: Sales Data Analysis (2021)
Performed EDA on sales datasets to detect trends, seasonality, and key drivers, highlighting potential areas for performance improvement.
Key Skills Demonstrated
- Exploratory data analysis and trend detection
- Data cleaning and preprocessing
- Visualization of time-series and categorical insights
- Identification of seasonality and performance drivers
- Generating actionable business recommendations from data
Interactive Notebook: View & Run on Kaggle | View Rendered Notebook on GitHub
Kaggle EDA with Python: Medical Data Visualizer (2021)
Visualized health-related metrics using Python libraries to reveal patterns and support data-informed analysis in health contexts.
Key Skills Demonstrated
- Data visualization with Python (Matplotlib/Seaborn)
- Creating categorical plots and correlation heatmaps
- Pattern discovery in health-related datasets
- Effective communication of complex relationships through visuals
Google Data Analytics Capstone: Bellabeat Case Study (2021)
Analyzed Fitbit user data to explore activity, sleep, and health patterns; delivered visualizations and recommendations to support product strategy and user engagement.
Key Skills Demonstrated
- End-to-end data analysis process
- Data cleaning and preparation
- Exploratory analysis and visualization (R)
- Insight generation and strategic recommendations
- Data storytelling through presentations