Devaki

Developed custom Python scripts for data extraction, transformation, and loading (ETL), leading to decrease in manual labor by automating intricate data processes.
Utilized SQL to create innovative stored procedures and triggers, significantly improving data reliability and automating maintenance tasks.
Led analytical projects following Agile (SCRUM) methodologies, consistently achieving on-time delivery rate, ensuring projects were completed efficiently within scope.
Performed Data Analysis and Data profiling using complex SQL on various sources systems including MS SQL Server.
Integrated Tableau with multiple data sources, including databases, Excel files, and cloud storage, to consolidate and visualize data from diverse platforms in a single dashboard.
Analyzed healthcare datasets using Python and its libraries, including NumPy, Pandas, SciKit-Learn, Matplotlib, Seaborn and Plotly, driving informed decision-making in healthcare management.
Proficient in using advanced Excel functions, including VLOOKUP, HLOOKUP, INDEX-MATCH, and SUMIFS for data manipulation and analysis, enabling quick and accurate data retrieval.
Crafted and implemented comprehensive dashboards using Tableau, employing various chart types for clear data presentation, leading to improved patient-caregiver engagement and operational cost savings.
Engineered automated data cleansing pipelines in Python, reducing data cleaning time and enhancing data accuracy through the detection and correction of data anomalies.
Efficiently used Power Query for establishing connections and transforming data from various sources, streamlining data preparation processes and elevating data accuracy standards.
Utilized Amazon EC2 instances to perform data analysis and processing tasks, leveraging the scalable computing power of the cloud for efficient data manipulation and modeling.
Employed Amazon S3 buckets for secure and scalable data storage, ensuring data availability and durability while facilitating data sharing and collaboration among team members.

• Developed custom Python scripts for data extraction, transformation, and loading (ETL), leading to decrease in manual labor by automating intricate data processes.
• Utilized SQL to create innovative stored procedures and triggers, significantly improving data reliability and automating maintenance tasks.
• Led analytical projects following Agile (SCRUM) methodologies, consistently achieving on-time delivery rate, ensuring projects were completed efficiently within scope.
• Performed Data Analysis and Data profiling using complex SQL on various sources systems including MS SQL Server.
• Integrated Tableau with multiple data sources, including databases, Excel files, and cloud storage, to consolidate and visualize data from diverse platforms in a single dashboard.
• Analyzed healthcare datasets using Python and its libraries, including NumPy, Pandas, SciKit-Learn, Matplotlib, Seaborn and Plotly, driving informed decision-making in healthcare management.
• Proficient in using advanced Excel functions, including VLOOKUP, HLOOKUP, INDEX-MATCH, and SUMIFS for data manipulation and analysis, enabling quick and accurate data retrieval.
• Crafted and implemented comprehensive dashboards using Tableau, employing various chart types for clear data presentation, leading to improved patient-caregiver engagement and operational cost savings.
• Engineered automated data cleansing pipelines in Python, reducing data cleaning time and enhancing data accuracy through the detection and correction of data anomalies.
• Efficiently used Power Query for establishing connections and transforming data from various sources, streamlining data preparation processes and elevating data accuracy standards.
• Utilized Amazon EC2 instances to perform data analysis and processing tasks, leveraging the scalable computing power of the cloud for efficient data manipulation and modeling.
• Employed Amazon S3 buckets for secure and scalable data storage, ensuring data availability and durability while facilitating data sharing and collaboration among team members.

Utilized Python for advanced data analysis and visualization, developing scripts and algorithms with libraries like Matplotlib, Pandas, and NumPy to uncover insights, predict trends, and support data-driven decisions.
Skilled in using SQL for data cleaning and preprocessing tasks, such as handling missing values, duplicates, and data format transformation.
Conducted data analysis using SQL on various source systems, including MS SQL Server, to extract valuable insights and support data-driven decision-making.
Applied Python to real-world datasets for data manipulation and cleansing using Pandas, NumPy and employed Matplotlib and Seaborn for initial insightful visualizations.
Contributed to the development of predictive models with SciKit-Learn, enhancing business decision-making with tangible data insights.
Participated in the development of Python scripts to automate the customer query system, contributing to a reduction in client query resolution time.
Created complex Excel formulas and functions to perform data calculations, such as financial modeling, trend analysis, and forecasting.
Assisted with automating reporting functionality using Power BI tools, including Power BI reporting, Dashboards & Scorecards (KPI), and MySQL, Data warehouse data sources, streamlining reporting processes.
Developed various solution-driven views and dashboards by creating different chart types, including Pie Charts, Bar Charts, Tree Maps, Circle Views, Line Charts, Area Charts, and Scatter Plots in Power BI, fulfilling the reporting requirements.
Designed and developed Power BI graphical and visualization solutions for the majority of projects with business requirement documents, ensuring the creation of interactive dashboards that align with project goals and objectives.
Collaborated under senior management's guidance to assist in comprehensive data analysis, supporting data mining and mapping techniques with Python.
Applied meticulous data cleansing processes to ensure high reporting standards and support informed decision-making.

Developed and maintained reports and dashboards with QuickSight and Looker to provide valuable insights to internal teams such as marketing, finance, and product, enabling data-driven decision-making across various departments.
Developed custom Python scripts and algorithms to automate data processing tasks, resulting in significant time savings and improved data accuracy.
Created interactive and dynamic data visualizations using Matplotlib and Seaborn, allowing for better data exploration and presentation of insights to stakeholders.
Applied Python for statistical analysis, hypothesis testing, and predictive modeling, contributing to data-driven decision-making in various projects.
Query, clean, combine, and organize data from various sources, including databases, APIs, and external data providers, to support company-wide analytical needs for nearly all data-related tasks.
Ensured data quality and accuracy across multiple business platforms by implementing data validation processes, performing data cleansing activities, and monitoring data integrity across a wide range of projects.
Cleaned data and processed third-party spending data into maneuverable deliverables within a specific format with Excel macros and Python libraries such as NumPy, Pandas, and Matplotlib for a significant portion of data processing tasks.
Proficient in data transformation and cleaning within Power BI, ensuring data accuracy and consistency across reporting dashboards.
Collaborated with cross-functional teams to gather data requirements and translate them into effective data visualizations in Power BI, facilitating better communication of insights.
Developed and maintained SQL queries, scripts, stored procedures, and functions to extract and manipulate data efficiently and effectively.
Used Oracle's SQL Loader utility for bulk data loading, enhancing the speed and efficiency of data integration processes.
Utilized MS SQL Server's Integration Services (SSIS) to design and implement ETL (Extract, Transform, Load) processes, ensuring seamless data integration from various sources.

Employed agile Scrum techniques to enhance data analysis workflows, promoting rapid response to change and fostering a dynamic, collaborative environment for project development.
Led the data management initiatives for a boutique law firm, utilizing advanced Excel techniques and cloud solutions to streamline data organization and accessibility.
Innovatively applied Python's Pandas, NumPy, and Matplotlib libraries to construct and refine machine learning models, significantly boosting resource management efficiency by 15% through detailed graphical analysis and capacity planning.
Revolutionized data processing efficiency by introducing SQL Server Integration Services (SSIS) for ETL tasks and formulating sophisticated SQL queries to optimize data retrieval and manipulation.
Executed targeted data improvement strategies, successfully elevating report precision by 15% and enhancing client contentment levels by 20% through meticulous analysis and recommendations.
Undertook detailed Exploratory Data Analysis (EDA) on a vast dataset of 800k records, employing Excel and MySQL techniques to ensure high data accuracy and integrity.
Applied Python to automate the customer query system, contributing to a substantial 15% reduction in client query resolution time.
Utilized Python for statistical analysis, hypothesis testing, and predictive modeling, driving data-driven decision-making in various projects.
Collaborated with cross-functional teams to gather data requirements and translate them into effective data analysis solutions using Python.
Utilized Tableau and MS Office Suite, particularly Advanced Excel, to create interactive dashboards, visualizations, and reports for data analysis and presentation to stakeholders.
Created Tableau dashboards with responsive design to ensure optimal viewing across different devices and screen sizes.
Adopted Power Query for streamlined data cleansing operations, achieving a 40% cost reduction in tools while maintaining an 80% standard in data quality, significantly boosting operational efficiency and accuracy.
Leveraged the flexibility and scalability of Amazon EC2 and Amazon S3 to accommodate varying data volumes and processing demands in data analysis projects.
Conceptualized and deployed comprehensive dashboards on Tableau Server, dramatically reducing data loss and boosting the effectiveness of key performance indicators (KPIs), thereby enhancing organizational strategic insight and decision-making.