Seasoned Data Analyst with approximately 4+ years of hands-on experience in data cleaning, data wrangling, and transformation, ensuring that datasets are prepared and organized for thorough data analysis.
Experienced in …
Seasoned Data Analyst with approximately 4+ years of hands-on experience in data cleaning, data wrangling, and transformation, ensuring that datasets are prepared and organized for thorough data analysis.
Experienced in model evaluation, hyperparameter tuning, and ensemble methods to optimize predictive model performance using Python.
Experienced in utilizing AWS and cloud technologies for designing scalable and efficient data solutions, proficient in leveraging services like AWS Data Pipeline or Azure Data Factory for automating data movement and transformation, ensuring seamless integration and scalability in cloud environments.
Proficient in developing complex stored procedures, efficient triggers, and necessary functions, resulting in a 25% improvement in database performance.
Excellent experience in monitoring SQL Server performance tuning, leading to a 30% increase in SQL Server efficiency.
Expertise in designing ETL data flows using SSIS which resulted in a 20% improvement in data extraction and transformation efficiency from Access/Excel Sheets to SQL Server.
Developed a customer churn prediction model using Python, achieving an accuracy rate of 84% through logistic regression, SVM, Decision tree, and Naïve Bayes algorithms.
Designed, expanded, and executed high-impact analytics solutions, leveraging structured and unstructured data sets to enable clients to make better fact-based decisions, resulting in a 40% improvement in decision-making accuracy.
Utilized Snowflake as the primary data warehouse, optimizing large datasets for analysis and achieving a 25% increase in data processing efficiency.
Used DBT for data modeling, transformation, and documentation, streamlining the data pipeline and ensuring a 30% improvement in data consistency across the organization.
Created and maintained Tableau dashboards to visualize key performance indicators, enabling a 35% improvement in data-driven decision-making for cross-functional teams.
Involved in the application of various machine learning algorithms and statistical modeling techniques like Regression, Random Forest, Association Rules, and Support Vector Machine, leading to a 20% improvement in predictive model accuracy.
Created aggregated daily reports for clients based on data analysis from both client-provided and open data sources, resulting in a 15% improvement in report accuracy.
Utilized statistical methods, metrics, and quantitative techniques to validate data integrity, enhancing data quality by 20%.
Automated ingestion processes using Python and Scala scripts, extracting data from various sources such as APIs, AWS S3, Teradata, and Snowflake, resulting in a 30% reduction in manual effort.
Implemented and documented ETL strategies to populate the Data Warehouse from diverse source systems, improving data integration efficiency by 25%.
Leveraged visualization tools including Power BI, Tableau, and SQL dashboards for data exploration and visualization, increasing the data analysis efficiency by 20%.
Analyzed spatial data to prepare analysis reports based on geographical regions, providing valuable insights for decision-making processes.
Demonstrated proficiency in the full development cycle, utilizing Agile, Scrum, and Waterfall methodologies, resulting in streamlined project execution and delivery.
Proficient in Python and Microsoft Excel, with experience in SQL for generating queries for data extraction, improving data retrieval efficiency by 15%.
Built workbooks importing data and defining relationships using Excel, Oracle, and MySQL, providing recommendations for data cleaning, sorting, and gathering processes, enhancing project team productivity by 20%.
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