Swathi K

Chennai,TamilNadu

Summary

Experienced professional proficient in Python, SQL, data manipulation, analysis, Pandas, Numpy, and Matplotlib. Aiming to leverage expertise in a dynamic environment to contribute effectively. Eager to apply skills and acquire new knowledge to drive impactful results. Responsive expert experienced in monitoring database performance, troubleshooting issues, and optimizing the database environment for optimal efficiency. Possesses strong analytical skills, excellent problem-solving abilities, and a deep understanding of database technologies and systems. Equally confident working independently and collaboratively as needed and utilizing excellent communication skills to foster effective teamwork.

Overview

Certification

Work History

Full Stack Developer Intern

Mr Cooper

08.2023 - 09.2023

Worked with senior developers and designers to implement Placement Portal project using HTML, CSS, JavaScript and PHP.
Enhanced user experience by developing responsive web designs for seamless cross-device compatibility.
Wrote production-ready code with fluency in modern front-end and back-end frameworks.
Learned new emerging technologies and took initiative to offer technical direction and creative solutions.

Education

BS(Online) - Data Science

Indian Institute of Technology - IIT Madras

Chennai, India

09.2027

B.E. - Computer Science

Einstein College of Engineering

Tirunelveli, India

08.2024

Skills

Python
Data Manipulation
Data Analysis
Pandas
Numpy
Matplotlib
Statistics
Problem Solving
Data analytics

Data visualization
Machine Learning
Teamwork and Collaboration
Data Warehousing
Data Modeling
ETL development
SQL Expertise
deep learning

Websites

Certification

Microsoft Certified: Azure Data Scientist Associate, Microsoft
Data Science & Analytics, upGrad Campus
Artificial Intelligence & Machine Learning, upGrad Campus

PROJECTS

Automated Bird Species Identification Using Audio Signaling

Analyzed bird song datasets, extracted audio features, and applied machine learning algorithms for species classification. Attained a 95% accuracy rate during model training.

Timeline