Computer Science Data Management

EDUCATION

Sandeep Kairamkonda

********************@*****.*** 312-***-****

CONCORDIA UNIVERSITY Milwakee, Wisconsin

Master of Science in Computer Science, GPA: 3.24 May 2024 AVN INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad, India

Bachelor of Technology in Computer Science, GPA: 3.4 June 2020 SKILLS

Programming & Development: Proficient in Python, SQL, C, Java, HTML, CSS, and RDBMS for application development and database management.

Cloud Technologies: Hands-on experience with Amazon Web Services (AWS) and Microsoft Azure for deploying cloud solutions and managing resources.

Data Visualization & Analysis: Skilled in creating interactive dashboards and visualizations using Tableau to present data insights effectively.

Data Management: Expertise in data clean-up, classification, and managing ETL workflows to ensure data quality and consistency.

Highlights: Problem-solving, Data Analysis, Innovative thinking, Attention to detail, Time management, Adaptability, Teamwork.

SIGNIFICANT PROJECT WORK

Health & Fitness Monitoring System

Designed an application to track dietary habits, workout routines, and monitor body measurements, utilizing MySQL for backend data management.

Developed real-time insights and interactive front-end features using PHP, HTML, CSS, and JavaScript for a smooth and responsive user interface.

Incorporated user authentication and data encryption techniques to ensure secure storage of personal health information.

Analysis of NYC Traffic Incidents Using Microsoft Azure

Led an extensive data analysis project leveraging Azure Databricks, Azure Data Factory, and Azure Synapse Analytics.

Managed ETL pipelines, performed detailed data exploration, and built predictive models aimed at improving traffic incident prevention measures.

Developed custom dashboards and visualizations for stakeholders to monitor key traffic trends and safety indicators in real-time.

Crime Data Insights & Offender Prediction

Analyzed crime data using Python to detect trends and patterns, supporting more effective law enforcement and data-driven decision-making.

Offender Prediction Model: Applied machine learning algorithms (Multilinear Regression, K-Nearest Neighbors, Neural Networks) to predict offender profiles, significantly enhancing the accuracy of crime- solving efforts.

Automated data cleaning and preprocessing steps, improving model efficiency and reducing time spent on data preparation by 40%.

LEADERSHIP

NSS Student Coordinator AVNIET August 2018 - February 2020 Public Relations Head Tech Innovators Club, AVNIET October 2019

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