Varsha Medukonduru

Organized and dependable person successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals.

• Data Management for Business Leaders
• Business Intelligence & Analytics
• Business Application Development
• Project Management
• Marketing Strategy
• Leadership & Organizational Behavior
• Financial Management
• Data mining in Business
• Statistics for Business
• Financial & Managerial Accounting
• Data Science for Business
• Machine Learning & Artificial Intelligence
• Enterprise Systems
• Database Development & Administration
• Technology and Operations Management
• Information Systems Security
• Analysis, Modeling and Design
• Distribution and Inventory Management
• IT and Supply Chain Management integrate analytics into marketing and communications strategies

• Tableau & Data Visualization: Analysis of the largest companies in the world, Tableau was used to convert raw data into understandable reports, improving communication. Conducted detailed analysis to find industry trends and potential customers across many industries. Collaborated with data engineers to create visual representations of complex datasets. Developed interactive dashboards using Power BI. Present findings to stakeholders to support strategic decision-making.
• Natural Language Processing: Classification model to accurately identify whether tweets refer to real disasters, Analyzed and visualized a dataset of 10,876 tweets obtained from Kaggle, focusing on key attributes like ID, keyword etc. Cleaned and prepared the dataset by removing URLs, symbols, emojis, and handling null values. Implemented various classifiers and selected the best performing model by utilizing techniques such as tokenization, stemming, and vectorization. Evaluated model performance using metrics like F1 score and confusion matrix, achieving a balanced precision and recall. Used Python (NLP libraries: NLTK, Keras), TensorFlow (for LSTM model with MirroredStrategy for GPU distribution and data visualization tools for exploratory data analysis (EDA).
• Data Science for Business: Predictive Pricing Model for Used Car, Conducted exploratory data analysis to identify patterns and trends in the data. Performed data preprocessing and preparation using Python libraries such as pandas, NumPy, and seaborn. Built and evaluated multiple regression models to determine the most significant variables affecting car prices. Compared model performance using statistical metrics (R-squared, Adjusted R-squared, F-statistic, AIC, BIC) to identify the best-fit model.
• Obstacle Avoiding Robot Using IoT, Designed and implemented an Obstacle Avoiding Robot using IoT technologies, integrating Google Automation for advanced control and monitoring capabilities. Utilized microcontrollers and sensors to enable real-time data collection and processing, ensuring accurate obstacle detection and avoidance.
• MATLAB: Design & Development of DC-DC Converter for Renewable Applications, Utilized MATLAB to create detailed simulation models, analyze transient responses, and optimize control algorithms for enhanced converter performance. Prepared comprehensive technical documentation, including schematics, simulation results, and performance metrics, to support product development and future iterations.