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Click here or scroll down to respond to this candidateEDUCATIONCandidate's Name
P: PHONE NUMBER AVAILABLE LinkedInSaharshaStreet Address @gmail.com Dallas,TXUNIVERSITY OF NORTH TEXASJan2023-May 2024Master of Science in Advanced Data AnalyticsGPA: 3.8 Relevant Coursework: Statistics with Excel and R, Harvesting, Storing and Retrieving Data, Large Data Visualization, Discovery and Learning with Big Data, Deep learning with Big Data, Recurrent Neural Networks Anna UniversityAug2016- Nov 2020Bachelor of Mechanical EngineeringGPA: 3.0WORK EXPERIENCESuzlon Energy LimitedJune2020-Dec2022 Designing and optimizing database performance with SQL queries and profiling, set up Data bricks, Azure AD and automated data workflows with Python and PySpark. Developed interactive dashboards in Tableau and Power BI, carried out EDA using Spark SQL and Data bricks SQL, and created logistical and manufacturing reports using Teradata SQL.` Implemented Agile (Scrum) methodologies and served as a member of a technical team to deliver the project in Python, SQL, and Tableau. Served as a bridge between the technical team and business stakeholders in an SDLC environment. Performed due diligence on business analysis for process improvements; worked with data governance teams on maintaining data quality; performed detailed data reviews and common mining in SQL and Python. Developed solutions with Hadoop, Spark, Hive and Kafka (and other big data technologies). Deployed Azure for analytics/processing and data storage built capable of serving clients seeking both cloud based and on premise data solutions. RSEARCH CAPSTONE EXPERIENCEPredictive Modeling For Hospital ReadmissionsJan 2024 May2024 Developed a Machine Learning model using logistic regression and random forest algorithms to predict 30 days hospital readmission risks for patients with chronic conditions utilizing electronic health care (HER) records. Implemented data preprocessing and EDA on patients variables to identify key predictors for readmission Designed an interactive dashboard using power BI to visualize readmission risk factors and trends to enable health care providers intervene with high risk patients effectively. TECHNICAL SKILLSLanguages: Python (NumPy, Pandas, Matplotlib, Scikit-learn), R, SQL Databases: PostgreSQL, Microsoft SQL Server, My SQL, Mongo DB Big Data Technologies: Apache Spark (Spark SQL, MLlib, Pyspark), Hadoop, Hive, Apache KafkaETL and Visualization: Airflow, Tableau, Power BI Quick Sight, Microsoft Excel, Visual Basic for Application (VBA), SASCloud: Microsoft Azure, AWS, DatabricksOther: Data Modeling, Big Data, Machine Learning, Natural Language Processing (NLP) and Neural NetworksACADEMIC PROJECTSClassification of Musical Genre Take into account applying some empirical Data Preprocessing and Exploratory Data Analysis on a dataset before spinning up a Machine Learning model. Classifying the sound to dance music: after the Data Wrangling and Feature Engineering steps we performed above, we applied some KNN algorithms recipes and our final model is a Random Forest, to detect the genre in the audio and extract meaningful information about the patterns that define a type of music. Time Series Forecasting of U.S Air Pollution LSTMs were used to conduct time series forecasting on the air pollution records, as well as recurrent neural networks (RNNs), showing that LSTMs are superior in capturing the long-term dependencies. With the development and deployment of predictive models, the goal is to gain high precision of forecasts for anticipatory governance of the environment.Data Visualization and Presentation using Tableau Deployed Tableau to gather useful information from a large database of Spotify tracks. Dashboard with useful insights and understandable data visualizations has been created for well informed decision making. |
