Tejaswini Budanam

Data Science Master's graduate with strong expertise in data analysis, statistical modeling, and data visualization. Proficient in SQL, Python, Excel, and data mining techniques, with the ability to analyze large datasets and provide actionable insights. Experienced in transforming complex data into strategic recommendations to support data-driven decision-making. Strong communication skills to present findings effectively to stakeholders and collaborate across teams. Seeking an entry-level Data Analyst role to apply analytical skills, contribute to data-driven strategies, and grow within a dynamic organization.

Master's Project:

Classification Of Heart Sound:

Objective: Designed and implemented a machine learning-based system to classify heart sounds (e.g., normal, murmur, or other abnormal sounds) for early detection of cardiovascular diseases.

Key Responsibilities:

• Data Collection and Preprocessing: Collected heart sound data (e.g., PCG signals) from publicly available datasets and performed noise reduction and segmentation.
• Feature Extraction: Analyzed heart sound signals using signal processing techniques (e.g., Fourier Transform, Wavelet Transform) to extract features such as frequency components and temporal patterns.
• Model Development: Developed and trained machine learning models (e.g., CNNs, RNNs) for sound classification, achieving high accuracy in distinguishing between normal and pathological sounds.
• Validation and Testing: Evaluated the model using metrics like accuracy, precision, recall, and F1-score. Utilized k-fold cross-validation to ensure robustness.
• Tools and Technologies: Python, Machine Learning Techniques , MATLAB, Librosa, scikit-learn.
• Outcomes: Achieved a classification accuracy of 89% on benchmark datasets, demonstrating potential for clinical applications in cardiac diagnostics.

Key Skills Demonstrated:

• Signal processing and feature engineering
• Machine learning and deep learning model development
• Domain knowledge of cardiovascular physiology
• Data analysis and visualization

Bachelors' project :

Developed a web-based system to streamline administrative and academic operations for schools. Key features included student registration, attendance tracking, grade management, and teacher-parent communication. Built using PHP, MySQL, HTML, CSS, and JavaScript, improving operational efficiency by 82% . Demonstrated expertise in full-stack development and database management.

• Microsoft Certified: Power BI Data Analyst Associate
• Microsoft Certified: Azure Fundamentals
• Microsoft Certified: Azure Data Fundamentals