Development Engineer Software

Prathyusha Keerthi

+1-757-***-**** ******************@*****.*** https://www.linkedin.com/in/prathyushakeerthi/ TECHNICAL SKILLS

Programming Languages/Tools C, JAVA, Python, Tableau, Informatica Web Technologies React, React Native, HTML, CSS, Cognos Databases & Servers MySQL

Additional Skills Data Analysis, Data Visualization, GIS Analysis EDUCATION

Master’s in Computer Science Aug’ 23 – Apr’ 25

Old Dominion University, Norfolk, VA, USA CGPA: 3.8/4 Bachelor of Technology in Computer Science Jun’18 - May ’22 KL University, India

PROFESSIONAL EXPERIENCE

CGPA: 8.45/10

Programmer Analyst Trainee (Cognizant- Hyderabad, India) Jan 2022 – Aug 2023

• Worked on developing and implementing ETL workflows using Informatica PowerCenter for seamless data integration and migration, while designing and maintaining enterprise-level reports and dashboards in Cognos to provide actionable insights.

• Ensured data quality and optimized performance of ETL processes and reports through tuning and validation. Collaborated with cross-functional teams to translate business requirements into scalable technical solutions, provided production support to minimize downtime, and documented workflows to streamline knowledge transfer and onboarding.

Tech Stack: SQL database, Informatica integration tool, Cognos tool Software Development Engineer (Techimax- Hyderabad, India) May 2021 - Dec 2021

• Served as a Software Development Engineer Intern at Techimax, focusing on data cleaning, algorithm testing, and optimization techniques.

• Applied algorithms including the Seasonal Naive model, Autoregressive Integrated Moving Average (ARIMA Model), and Linear Regression to evaluate and predict sales data.

• Utilized these algorithms to identify the most effective approach for analyzing and forecasting sales data, enabling data-driven decision-making.

Tech Stack: Jupyter notebook, Python

ACADEMIC PROJECTS

Movie Recommendation System

Developed a personalized movie recommendation system using PySpark and Spark MLlib’s ALS algorithm, optimizing parameters for collaborative filtering with RMSE evaluation. Pre-processed data by handling missing values, normalizing ratings, and expanding multi-genre movies for granular analysis. Dynamically generated user-specific recommendations and incorporated feedback loops for continuous model improvement. Built a scalable architecture capable of handling large datasets and real-time interactions, ensuring deployment readiness. Tech Stack: PySpark, Spark MLlib, ALS Model.

Water Quality Data Normalization and Ayalysis

Designed and implemented a normalized database schema for water quality data, creating six interrelated tables to optimize data storage and querying efficiency. Executed SQL queries to answer complex business questions, such as monthly averages, extreme values, and data consistency across stations. Created an ERD diagram to represent the relationships between tables, ensured data integrity through normalization and validation. Tech Stack: MySQL, SQL Queries, ERD design.

Alzheimer’s Disease and Health Aging

Applied machine learning and statistical techniques to analyze trends, prevalence, and risk factors related to Alzheimer's disease. Conducted geospatial analyses using GIS tools to map disease distribution and identify regional patterns. Used time-series analysis to evaluate the temporal evolution of Alzheimer’s prevalence and its societal impact. Tech Stack: Python, Tableau, SQL database, GIS tools. Google Food Review system

Analyzed restaurant reviews to identify dish popularity and quality using machine learning algorithms. Developed a Google extension for seamless website integration and data scraping. Provided data-driven insights to help customers make informed choices.

Tech Stack: React, Data scraping, Python.

Comparative Analysis of Crime Rates during COVID-19 Pandemic Analyzed crime trends in Norfolk and Dallas (2017-2020) to assess the pandemic’s impact on criminal activities. Identified the escalation of specific offenses during this period, offering valuable insights into public safety. Tech Stack: Python, Tableau, SQL database.

Stock price Visualization and Analysis

Cleaned and processed daily stock price datd to visualization performance trends across companies.identified best- performing stocks using data visualization techniques and SQL analysis. Tech Stack: Python, Tableau, SQL database.

Car Rental Application

Designed a user-friendly car rental platform with features like browsing, filtering, and personalized user portals. Incorporated a seamless transaction system and model-specific search functionality. Tech stack: React Native, node.js.

Music Application

Developed a music application with personalized user experiences, playlist curation, and mobile accessibility. Added QR code scanning for easy application access on mobile devices. Tech stack: React Native, node.js.

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