Nibedita Sahu

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About Me

Hi, I'm Nate (short for Nibedita), a Data Scientist with a strong foundation in Mathematics. I specialize in building end-to-end data-driven systems using Python, SQL, ML, and Statistics to analyze, model, and interpret data. With a passion for problem-solving and data storytelling, I focus on transforming complex data into clear and actionable insights. I'm particularly interested in the intersection of Data Science and AI, integrating intelligent systems to support real-world decision-making. I'm constantly learning and refining my skills, working on projects that challenge me to grow and explore new opportunities.



View Resume

Skills

Data Science & Machine Learning

Programming ⇰ Python SQL

Data Analysis ⇰ NumPy Pandas

Visualization ⇰ Matplotlib Seaborn

ML ⇰ Scikit-learn StatsModels SciPy

Databases ⇰ MySQL PostgreSQL

BI ⇰ Power BI Tableau Excel

Statistics & Modeling
Data Preparation Data Transformation Exploratory Analysis Feature Engineering Feature Selection Probability & Statistics Hypothesis Testing Mathematical Modeling Machine Learning Model Selection Predictive Modeling Model Validation Model Interpretation Time Series Analysis Forecasting
Analytics & Decision Intelligence
Data Analysis Data Interpretation Insight Generation Data Visualization Data Storytelling Decision Making Problem Solving Performance Analysis AI Integration LLM APIs Analytical Thinking Strategic Thinking
Workflow & Communication Tools

Version Control ⇰ Git GitHub

Development ⇰ VS Code Jupyter Colab

Documentation ⇰ Markdown LaTeX

Web Basics ⇰ HTML CSS

Presentation ⇰ PowerPoint Canva

Background

🎓 Bachelor of Science in Mathematics

With a strong analytical mindset shaped through my academic journey, I've developed a natural inclination toward solving data-driven problems. My degree has helped me understand the logic, structure, and patterns that form the backbone of Data Science & Machine Learning.


Kalahandi University, Bhawanipatna, Odisha
2020-2023

Experience

Independent Data Science Researcher & Technical Writer | Medium

May 2023 - Present
  • Maintain a technical research portfolio on Machine Learning and Statistics, with articles accepted into specialized Data Science publications that verify content quality before distribution.
  • Architect end-to-end project walkthroughs that decompose complex Machine Learning lifecycles into interpretable, modular components for the data community.
  • Synthesize emerging trends in Generative AI and LLM integration, documenting practical frameworks for leveraging AI in business intelligence and decision support.
Data Projects DS Playground ML for DS NumPy for DS & DA Pandas for DS & DA Explore More

Technical Content Writer | GeeksforGeeks

Sep 2023 - Sep 2024
Gaussian Mixture Models (GMM) in Scikit Learn Quick Start to Gaussian Process Regression Draw Multiple Y-Axis Scales In Matplotlib FULL OUTER JOIN in SQLite Pagination in SQL Explore More

Core Projects

Customer Retention & Revenue Optimization System

Built an end-to-end data science system to identify high-value customers, predict churn and purchase behavior, and optimize targeting strategies to maximize revenue under budget constraints. The project focuses on turning predictions into actionable business decisions with measurable impact. It combines data engineering, modeling, and optimization into a structured workflow that reflects real-world decision-making.

GitHub
Notebooks SQL SRC Models Data
Reports Project Walkthrough
Project 5 Image
Lab Project 3

End-to-End Time Series Analysis & Forecasting with Nutrition Data

A fully documented Time Series Analysis project built on a realistic multi-year nutrition dataset. The project progresses from dataset design and proper time handling to trend, seasonality, and variability analysis, followed by simple, interpretable forecasting. It focuses on understanding real-world time-based behavior, avoiding common pitfalls, and communicating insights clearly through visual storytelling.

GitHub
Notebook-0 Notebook-1 Notebook-2 Notebook-3
Project Walkthrough Time Series 101 Power BI (Static) Power BI (Interactive) Tableau (Static) Tableau (Interactive)
Project 4 Image

Electric Vehicle Population Analysis

A complete end-to-end data analysis project exploring Electric Vehicle adoption. Used SQL, Python, and Power BI to clean, analyze, and visualize trends in EV types, range, and policy eligibility. Delivered actionable insights on top-performing models, regional adoption, and CAFV alignment through a multi-page report and presentation.

GitHub
Jupyter Notebook SQL Script
Static Report Interactive Report Presentation
View More Projects

Explore Case Studies

Here you'll find detailed breakdowns of the projects and problems I've worked on. Some focus on individual challenges, while others explore projects I've built, walking through the decisions, steps, and reasoning behind each approach. Each case study gives a clear picture of how I approached a problem, what I learned, and the insights that came out of it. Whether you're exploring solutions, curious about how things work, or just enjoy seeing problems being solved, there's something here for you.
Project-Based Problem-Based

Lab ⇝ Experiment • Build • Explore

AI-Powered Marketing Campaign Intelligence System

Built an AI-powered system that transforms marketing campaign metrics into actionable business insights. Simulates user behavior, extracts key performance indicators, and generates strategic recommendations using LLMs with a fallback mechanism, demonstrating the last mile of analytics, turning data into decisions.

GitHub
Data Simulation Metrics Extraction AI Insights Fallback Run Pipeline
Read Article
Lab Project 4

Regression Framework for Systolic Blood Pressure Prediction

A fully documented Regression workflow to predict Systolic Blood Pressure using Age, BMI, Activity, and Salt Intake. The project progresses from simple to multiple regression, manual β-calculation, and a final scikit-learn model. It focuses on clarity, interpretability, and comparing different modeling approaches, not just running code. Perfect as a reusable framework for Linear Regression.

GitHub
SLR MLR (2 Predictors) MLR (4 Predictors) Manual Beta Computation Scikit-learn SciPy x NumPy
Read Article
Lab Project 2
Try This Web App - Body Fat Calculator

This web app calculates Body Fat %, Fat Mass (kg), and Lean Mass (kg) interactively. Built with Pandas & Streamlit!

GitHub Open Live App Read Article


View More Experiments

Contact Me