Highly skilled Data Analyst with 3+ years of experience driving successful business solutions through data interpretation and analysis. Proficient in data management, statistical analysis, and visualization using Excel, SQL, Python, and Power BI. Expertise in building predictive models, identifying trends, and offering actionable insights to improve decision-making. Strong problem-solving skills with a record of enhancing operational efficiency. Passionate about leveraging data to optimize business strategies and drive long-term growth.
Programming & Scripting Languages: Python, C, C, MATLAB, R, Basic Core Java, Java script, SQL, PLSQL
Databases: MySQL, SQL Server, Postgres
Big Data Technologies: Hadoop, Spark, Hive, Impala, Machine Learning
Cloud services: Azure Data Bricks, Azure Data Factory, Azure Synapse, AWS, ETL pipeline development
Data Analytical Tools: Tableau, MS Excel, PowerBI
Movie Database and Stream lit Web App
• Designed and implemented a comprehensive movie database, integrating advanced data
engineering techniques, efficiently managing and processing over 10,000 movie records and
associated metadata.
• Constructed a user-friendly Stream lit web application to provide seamless access to extensive
movie information, enhancing user experience through efficient data organization and retrieval.
Skills acquired: SQL, Python, Stream lit webapp.
TalkingData Ad Tracking Fraud Detection
• Identified and validated optimal predictive model at TalkingData, China's leading big data service
platform, with 98% accuracy.
• Implemented model into a user-friendly Stream lit web app, reinforcing fraud detection capabilities
in real-time, resulting in a 40% reduction in false positives and a 20% increase in detection speed.
Skills acquired: Python, Stream lit webapp, Machine learning algorithms.
Image Colorization Using CNN
• Developed a CNN for image colorization using the LAB color space and transfer-learning on a
subset of MIT Places dataset. Employed ResNet-18 as model backbone, optimizing with Adam
optimizer and utilizing Mean Squared Error (MSE) loss during training.
Skills acquired: Pytorch, Python, EDA, Computer vision techniques.
Breast Cancer Classification Through Machine Learning
• Employed Python in tandem with machine learning algorithms, including Logistic Regression,
Decision Tree, and Random Forest, to predict and classify breast cancer as either malignant or
benign.
Skills acquired: Python, Statistical methods, Pyplot and seaborn visualization.
Customer Churn Prediction with Artificial Neural Network In Banking
• Developed a model utilizing artificial neural networks to classify and forecast customer churn,
enhancing retention strategies and proactive customer management.
Skills acquired: Python, Statistical methods, EDA.