Nishanth Sura

• Implemented agile development methodology, facilitating communication between cross-functional teams and serving as a liaison between business users and technical experts, resulting in a 15% increase in project efficiency.
• Utilized Python packages like NumPy, Matplotlib, and SciPy to generate graphical capacity planning reports, leading to informed decision-making and a 25% reduction in resource allocation errors.
• Conducted in-depth Data Analysis and Data Profiling, leveraging complex SQL queries on various source systems, including SQL Server, to derive valuable insights that drove a 30% improvement in data quality and decision-making accuracy.
• Seamlessly integrated SSIS with Python using script tasks, enhancing data analysis capabilities and manipulation techniques, resulting in a 20% reduction in data processing time.
• Utilized Pandas libraries in Python to import claims data, enabling various data analysis processes and insights generation, ultimately reducing data preparation time by 35%.
• Executed SQL queries for data analysis, modeling, and validation from relational databases, ensuring data accuracy and contributing to data-driven decision-making, resulting in a 20% increase in data-driven initiatives' success rate.

<p>Implemented agile development methodology, facilitating communication between cross-functional teams and serving as a liaison between business users and technical experts, resulting in a 15% increase in project efficiency.<br>
Utilized Python packages like NumPy, Matplotlib, and SciPy to generate graphical capacity planning reports, leading to informed decision-making and a 25% reduction in resource allocation errors.<br>
Conducted in-depth data analysis and data profiling, leveraging complex SQL queries on various source systems, including SQL Server, to derive valuable insights that drove a 30% improvement in data quality and decision-making accuracy.<br>
Seamlessly integrated SSIS with Python using script tasks, enhancing data analysis capabilities and manipulation techniques, resulting in a 20% reduction in data processing time.<br>
Utilized Pandas libraries in Python to import claims data, enabling various data analysis processes and insights generation, ultimately reducing data preparation time by 35%.<br>
Executed SQL queries for data analysis, modeling, and validation from relational databases, ensuring data accuracy and contributing to data-driven decision-making, resulting in a 20% increase in data-driven initiatives' success rate.<br>
Demonstrated expertise in data cleansing, validation, and transformation, utilizing ETL methodologies to improve data quality and consistency for analysis and reporting purposes, reducing data errors by 25%.<br>
Built and published customized interactive reports and dashboards using Tableau, ensuring accessibility and report scheduling for stakeholders, resulting in a 40% improvement in data accessibility and understanding.<br>
Successfully extracted data from PostgreSQL databases, contributing to the creation of SQL queries, stored procedures, and common table expressions (CTEs) that played a pivotal role in multiple departmental reports, increasing report generation efficiency by 20%.<br>
Engineered complex Tableau reports by employing functionalities like context filters, hierarchies, and LOD expressions, improving report accuracy by 20% and providing deeper insights into data trends.<br>
Created business metric KPIs (Key Performance Indicators) to evaluate module-specific factors, enhancing performance assessment and enabling a 15% increase in targeted performance improvements.</p>

<p>Demonstrated expertise in data analysis, data warehousing, data migration, data cleaning, statistical modeling, and SQL programming, actively contributing to a 20% reduction in decision uncertainty.<br>
Leveraged Python's statistical libraries to conduct hypothesis testing, significance testing, and confidence interval estimation, achieving a 95% confidence level in hypothesis testing.<br>
Implemented sophisticated data cleaning pipelines in Python, leading to a 25% reduction in data errors and a 30% increase in data accuracy and consistency.<br>
Utilized R for data acquisition and data integrity, encompassing datasets comparison and dataset schema checks, reducing data discrepancies by 15% through systematic data schema validation.<br>
Crafted visually engaging charts, graphs, and plots using Matplotlib and Seaborn in Python, improving data comprehension by 25% and resulting in quicker decision-making.<br>
Imported the customer data into Python using Pandas libraries and performed various data analysis, finding patterns in data which helped in key decisions for the company.<br>
Played a key role in creating and maintaining database objects (tables, views, procedures, triggers, and functions) using SQL to optimize data management and enhance efficiency.<br>
Implemented ETL methodology to support data extraction, transformations, and loading processes within a complex EDW using Informatica, reducing data processing time by 40% and improving data accuracy by 15%.<br>
Deployed MongoDB's geospatial capabilities to execute spatial queries, providing valuable location-driven insights and increasing location-targeted marketing effectiveness by 10%.<br>
Provided hands-on support to users in creating and modifying worksheets, data visualization dashboards, and Power BI reports, enhancing data analysis capabilities.<br>
Developed Power BI data visualization using Cross tabs, Heat maps, Box and Whisker charts, Scatter Plots, Geographic maps, Pie Charts, Bar Charts, and Density charts.<br>
Leveraged MS Excel for data rules spreadsheets and report preparation, including VLOOKUP, HLOOKUPS, pivot tables, Macros, and data points.<br>
Utilized SSAS and SSIS to enhance data analysis capabilities, defining calculated measures, KPIs, perspectives, and processing large data volumes for reporting and analysis.</p>