Jyothi Bhavani Patibandla

• Developed data preprocessing ETL pipelines that meet all the business requirements to standardize and prepare data for analysis, ensuring data consistency. • Conducted Data collection, Data Cleaning, Data Transformation, Exploratory data analysis, to the transformed data to derive key insights further Designed and developed interactive data visualization dashboards using Power BI and Microsoft Excel to facilitate data-driven decision-making processes to the stakeholders. • Applied SQL expertise for effective database design, optimization, and query performance tuning in Azure environment, including utilization of Microsoft SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS) for data integration. • Utilized Apache Airflow for data orchestration, ensuring efficient and automated data workflows for large volumes of data. Used Microsoft Azure tools such as Azure Data Factory, Azure Blob Storage, and data warehouses to manage and integrate large volumes of data. • Utilized Jira for project management, issue tracking, and workflow management, ensuring efficient coordination, collaboration and communication among team members, while also leveraging Git for version control and collaborative development.

• Developed a system to detect face masks using Artificial intelligence and Machine learning methods. Gathered a dataset that is diverse and representative of real-world scenarios containing images of faces of people, some with masks and without masks. • Worked on diverse technologies likes Python, Keras, OpenCV • Engineered intricate CI/CD pipelines to extract meaningful features through Feature Engineering to ensure consistency in size, color, and orientation from the facial images. • Conducted thorough model testing to assess performance, analyzing predictive and prescriptive insights using classification reports along with confusion matrix in Jupyter notebook. • Actively participated in ongoing research and development efforts to enhance the accuracy and robustness of the face mask detection system.