About Me
My journey to becoming a Data Scientist wasn’t linear — it looked more like a beautifully complex neural network that finally converged.
With a B.Tech in Electronics & Communication and a Master’s in Digital Communication, I had the technical foundation. But instead of diving straight into code, I began by analyzing a far more unpredictable dataset: people.
As a lecturer, I ran daily exploratory data analysis on student performance, engineered behavioral features, and identified hidden variables affecting outcomes — long before I knew those terms formally.
In project coordination and HR, I optimized workflows, debugged communication gaps, and performed root-cause analysis on team dynamics — essentially running real-time process mining without Power BI.
Then came motherhood — my most demanding longitudinal research project. Two children, infinite variables, continuous A/B testing on routines, and advanced resilience modeling under extreme sleep deprivation conditions. That “career break” wasn’t a gap. It was model refinement — strengthening emotional intelligence, long-term forecasting, and decision-making under uncertainty.
And then the insight emerged — clean and unmistakable: I wasn’t changing careers. I was formalizing what I had always been doing. So I built the technical stack intentionally. I completed the IBM Data Science Professional Certificate, earned an offline Diploma in Data Science, and strengthened my statistical foundation through the Introduction to R Programming course.
I expanded into AI through Generative AI for Data Science, Google Prompt Essentials, and Prompt Engineering certifications from Google and Microsoft via Coursera — integrating machine learning, deep learning, Python, SQL, and applied AI into real-world projects.
Today, I translate complex datasets into actionable intelligence using Power BI and advanced analytics — turning numbers into narratives that drive decisions. Because whether it’s classrooms, boardrooms, households, or high-dimensional datasets, my mission remains constant:
“Transforming thoughts, experiences, and data into knowledge, solutions, and inspiration for the world.”
Professional Highlights
- Academic Foundation: B.Tech in Electronics & Communication Engineering and M.Tech in Digital Communication Engineering.
- Teaching Experience: Over 3 years of engineering teaching experience.
- Project & HR Experience: Professional experience in Project Coordination and HR operations.
- Data Science Training: 6-Month Offline Diploma in Data Science with ML, DL & Python exposure.
- International Internship: 1-Month International Internship in Power BI.
- Certifications: IBM Data Science Professional Certificate, Generative AI & Google Prompt Engineering.
Achievement Highlights
Research Publications
An Applied Statistical Study of Commercial Aircraft Safety
An exposure-adjusted statistical comparison of Boeing and Airbus using normalization techniques, chi-square testing, and Poisson modeling.
PreprintA Comparative Survey of Python and R for Data Science
A detailed comparison of Python and R focusing on libraries, execution performance, and statistical capabilities for data science.
Under ReviewKaggle Achievements
Titanic — Machine Learning from Disaster
Built a machine learning classification model on the Titanic dataset using feature engineering and hyperparameter tuning. Optimized a Random Forest model with 100 iterations to improve survival prediction accuracy through ensemble learning.
ParticipatedDiabetic Risk Prediction
Developed a machine learning classification model to predict diabetes risk using advanced data preprocessing, feature engineering, and imbalance handling techniques. Implemented Logistic Regression, Random Forest, and XGBoost, achieving a ROC-AUC score of 0.89 with optimized hyperparameters and cross-validation.
ParticipatedDigit Recognizer — Computer Vision Challenge
Implemented a CNN model using TensorFlow to classify handwritten digits from the MNIST dataset, achieving high validation accuracy.
ParticipatedCourses & Certifications
Diploma in Data Science
Completed a Diploma in Data Science with strong foundations in statistics, data analysis, and machine learning. Applied Python and Scikit-learn to build and evaluate predictive models using cross-validation and ROC-AUC. View CertificateIBM Data Science Professional Certificate
Comprehensive program with 12 modules covering Python, SQL, Data Analysis, Visualization, and Machine Learning.
View CertificateGenerative AI for Data Science
Certification by Microsoft. Explore diverse applications of GenAI, including data augmentation, feature engineering, anomaly detection, and generative modeling.
View Certificate