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Shiliveri Sai Jyothi

Entry-level Data Analyst • Bangalore, India • s*************@gmail.com • +91*******792 • linkedin.com/•••••

Career Objective

Recent B.Tech graduate in Computer Science with hands-on project experience in data analysis, NLP-based classification, and computer-vision OCR pipelines. Proficient in SQL and Python (Pandas, NumPy) and experienced with Power BI and Excel for reporting and visualization. Built and validated classification and real-time detection systems that improved content accuracy and reduced engagement with harmful content in project settings. Seeking an entry-level Data Analyst role to translate data into actionable insights, optimize reporting workflows, and support data-driven decision-making.

Education

Vaagdevi Institute of Technology and Science
Bachelor of Technology in Computer Science & Engineering • January 2020 – May 2024
Sri Shiridi Sai Junior College (Board of Intermediate Education)
Intermediate • June 2018 – May 2020

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,scikit-learn,OpenCV,Matplotlib,Seaborn
Databases: SQL,SQL queries,Relational data concepts
Data and Analytics: Data cleaning,Data preprocessing,ETL concepts,Supervised classification,Model evaluation,Power BI,Excel
Tools and Methodologies: Jupyter Notebooks,Statistical analysis,Hypothesis testing
Skills: Text preprocessing,NLTK,spaCy
Computer Vision & OCR: Tesseract OCR,Image preprocessing

Projects

Social Media Misinformation Detection and Democratic Verification Mechanism Design | January 2024 – May 2024
Tools Used: Python, Pandas, NumPy, NLTK, spaCy, scikit-learn, Power BI, Statistical analysis
  • Collected and cleaned large-scale social media interaction datasets to prepare features for classification and trend analysis, reducing noisy entries by 35%.
  • Engineered textual features and trained supervised classification models using scikit-learn to detect misleading content, achieving high precision in test validations.
  • Designed a democratic verification mechanism that cross-referenced multiple sources and consensus rules, improving verified content accuracy by 28%.
  • Developed NLP pipelines (tokenization, stopword removal, lemmatization) and evaluated models with precision/recall metrics to prioritize low false-positive rates.
  • Built dashboards in Power BI to visualize misinformation trends and statistical impact on public opinion, enabling clear stakeholder reporting.
Automation Recognition of License Plates | June 2023 – September 2023
Tools Used: Python, OpenCV, Tesseract OCR, Image preprocessing, Pandas
  • Implemented an automated license plate recognition pipeline using OpenCV for detection and Tesseract for OCR to extract plate characters reliably.
  • Applied image preprocessing techniques including adaptive thresholding and edge detection, improving detection throughput and robustness under varied lighting.
  • Optimized character segmentation and OCR integration to increase recognition speed and real-time processing capacity by ~30%.
  • Validated system performance with cross-validation on diverse datasets and quantified robustness improvements (~15%) in challenging lighting conditions.

Certifications

Mathematics for Data Science — Simplilearn
SQL for Data Analysis — Simplilearn

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