As a biologist turned data scientist, I have made an exciting transition to the world of data science. Fascinated by the
vast potential of working with data, I am driven to excel in this domain. With a strong background in aptitude, I bring a
unique perspective to problem-solving and an innate curiosity for exploring patterns and insights hidden within data.
Equipped with my newfound passion for data, I am eager to leverage my analytical skills, statistical knowledge, and
programming expertise to tackle complex challenges and contribute to impactful projects. My journey from biology to
data science reflects my adaptability and determination to thrive in a dynamic and rapidly evolving field. I am excited to
continue learning and growing as a data scientist, making meaningful contributions to the world of data-driven decision-
Analyzed Bacterial Promoter Sequence Data,
Statistical Analysis and Feature Transformation using various Parameters of DNA,
Built Machine Learning Model using SVM, Random Forest, XgBoost.
Improved Accuracy, Precision, Recall, F1 Score from 68% to 90% through Ensemble Techniques and Grid Search.
Provided Model Interpretability with Explainable AI using SHAP
Tezpur University, India 2021 - 2023
Google Data Analytics
- Gain immersive understanding of data analyst practices and processes.
- Learn key analytical skills and tools, including data cleaning, analysis, and visualization.
- Understand data cleaning, organization, and analysis using spreadsheets, SQL, and R programming.
- Learn visualization and presentation of data findings in dashboards and presentations.
- Gain familiarity with commonly used visualization platforms.
Machine Learning Specialization
- Build ML models using NumPy and scikit-learn for prediction and binary classification tasks, including linear and logistic regression.
- Utilize TensorFlow to construct and train a neural network for multi-class classification.
- Employ decision trees and tree ensemble methods for modeling and training purposes.
- Apply best practices in ML development and incorporate unsupervised learning techniques such as clustering and anomaly detection.