Atul Bhardwaj Profile Picture

Atul Bhardwaj

Data Science Graduate Student | Machine Learning Enthusiast | Turning Data into Impact

About Me

Driven by data. Focused on impact.

I'm a Computer Science graduate student specializing in Data Science at Seattle University, driven by the belief that data shouldn't just inform—it should create real-world change.

From predicting heart disease to optimizing customer retention, my projects focus on one thing: using data to solve problems that matter. Whether it's clustering patients for better care or visualizing campus trends to improve student services, I'm always looking for patterns that lead to impact.

I've worked across research, analytics, and mission-driven initiatives, publishing machine learning research, leading student outreach strategy, and contributing to ESG reporting efforts. My daily toolkit includes Python, SQL, Power BI, Tableau, and TensorFlow, but it's curiosity, empathy, and clarity that guide how I use them.

Right now, I'm exploring opportunities where I can apply data skills in service of people and the planet, especially in energy, healthcare, or public-interest spaces.

Professional Experience

Building impact through data-driven solutions

Data Science Consultant

Statistics Without Borders

September 2025 - Present | Seattle, WA

  • Drove strategic insights for Women in Sport by analyzing 14+ survey datasets (n > 2,200) using Python, SQL, and statistical modeling (logistic regression, factor analysis, clustering)
  • Translated complex statistical results into executive-ready visualizations and presentations, enabling non-technical stakeholders to make data-driven decisions
  • Mentored 7 cross-functional project teams on reproducible data workflows, statistical best practices, and compelling data storytelling

Data & Service Operations Assistant

Seattle University

June 2025 - Present | Seattle, WA

  • Reduced guest parking complaints by 80% by analyzing seasonal demand patterns in Power BI and Python
  • Applied Gaussian Mixture Model (GMM) clustering to segment 5 distinct departmental behavior patterns, reducing unnecessary support tickets by 25%
  • Automated data extraction from Microsoft Access databases using optimized SQL queries, improving data accuracy from 85% to 100%
  • Saved 30-40 staff hours per month through process automation and workflow optimization

Research Assistant

Seattle University

December 2024 - March 2025 | Seattle, WA

  • Conducted health risk prediction modeling research using machine learning techniques
  • Collaborated with faculty on developing predictive models for clinical decision support

Data Science Club Captain

Ganga Institute of Technology & Management

November 2022 - May 2024 | India

  • Led and motivated 100+ members of the Data Science Club, organizing successful webinars and events
  • Developed innovative strategies to enhance learning experience through guest speaker sessions, daily quizzes, and coding competitions

Featured Projects

Solving real-world problems with data and code

SmartStock Project Screenshot

SmartStock – Automated Inventory & Ordering SaaS

In Progress | September 2025

Cloud-native SaaS solution on Microsoft Azure, integrating Azure Functions, Cosmos DB, and App Service for real-time inventory tracking and automatic reordering. Features microservice-based APIs and Power BI dashboards.

Azure Cosmos DB Power BI Microservices
TF-IDF Search Engine Project Screenshot

Real-Time TF-IDF Search Engine

March 2025

Scalable distributed search engine on AWS infrastructure (EMR, EC2, S3, DynamoDB, Lambda) using Apache Spark for parallel processing. Implemented TF-IDF-based document ranking algorithm for fast, relevant search results.

AWS Apache Spark Python Big Data
Heart Disease Predictor Project Screenshot

Heart Disease Predictor

September 2024

Developed and compared 6 supervised learning models on 304 patient records, optimizing precision, recall, and F1-score to minimize false negatives for clinical decision support. Published findings in IEEE Xplore.

Machine Learning Python Healthcare Research

Technical Skills

Tools and technologies I work with

Programming Languages

  • Python
  • SQL
  • R
  • C++
  • Java
  • JavaScript
  • Node.js

Machine Learning

  • Scikit-learn
  • TensorFlow
  • PyTorch
  • Time-Series Forecasting
  • Clustering (K-Means, GMM)
  • Statistical Modeling

Data Analytics & Visualization

  • Tableau
  • Power BI
  • D3.js
  • Matplotlib
  • Seaborn
  • Plotly

Databases

  • PostgreSQL
  • MySQL
  • DynamoDB
  • CosmosDB
  • Hive (HQL)
  • Microsoft Access

Cloud & Big Data

  • AWS (EC2, EMR, Lambda, S3)
  • Azure (Functions, App Service)
  • Apache Spark
  • Hadoop
  • Distributed Systems

Professional Skills

  • Statistical Data Analysis
  • Data Storytelling
  • Cross-functional Collaboration
  • Leadership & Mentoring
  • Problem Solving

Publications

Research contributions to the field

Evaluating ML Algorithms for Heart Disease Prediction

IEEE Xplore, 2024 | Read Paper →

Comprehensive evaluation of six supervised machine learning algorithms for predicting heart disease, focusing on precision, recall, and F1-score optimization to minimize false negatives in clinical decision support systems.

Bidirectional LSTM for Toxic Comment Classification

EAI Journal, 2024 | Read Paper →

Novel approach using Bidirectional LSTM with Convolution for identifying and classifying toxic comments in online platforms, demonstrating improved performance in natural language processing tasks.

Certifications

Continuous learning and professional development

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AWS Cloud Practitioner

Amazon Web Services

Courses 1-4 | Expected: November 2025

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AWS AI Practitioner

Amazon Web Services

Courses 1-5 | Expected: November 2025

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Data Science Methodology

IBM / Coursera

2024

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Introduction to Machine Learning

Microsoft / LinkedIn Learning

2024

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What is Data Science?

IBM / Coursera

2023

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Tools for Data Science

IBM / Coursera

2023

Get In Touch

Let's connect and create something impactful together

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Location
Seattle, Washington, USA
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