Big Data, Machine Learning, ETL and ELT and Data Analysis

EDUCATION

SRIHARSHA CHITTAMURU

P: +1-615-***-**** LinkedIn

Saharsha63@gm

ail.com Dallas,

TX

UNIVERSITY OF NORTH TEXAS

Jan2023-May 2024

Master of Science in Advanced Data Analytics

GPA: 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 University

Aug2016- Nov 2020

Bachelor of Mechanical Engineering

GPA: 3.0

WORK EXPERIENCE

Suzlon Energy Limited

June2020-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 EXPERIENCE

Predictive Modeling For Hospital Readmissions

Jan 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 SKILLS

Languages: 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 Kafka

ETL and Visualization: Airflow, Tableau, Power BI Quick Sight, Microsoft Excel, Visual Basic for Application (VBA), SAS

Cloud: Microsoft Azure, AWS, Databricks

Other: Data Modeling, Big Data, Machine Learning, Natural Language Processing (NLP) and Neural Networks

ACADEMIC PROJECTS

Classification 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 algorithm’s 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.

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