Nagiripati Chakradhar Varma
Data Analyst (Entry-level) • Kottayam, Kerala, India • n*************@gmail.com • 833****304
Career Objective
Entry-level Data Analyst with a B.Tech in Computer Science from IIIT Kottayam. Strong foundation in Python and SQL with hands-on experience in data cleaning, exploratory data analysis, feature engineering, and basic machine learning from academic projects. Skilled with Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn and XGBoost; experienced using MySQL and SQLite for data handling and Git/GitHub for version control. Looking to apply analytical skills and produce actionable insights in a collaborative data team.
Education
Indian Institute of Information Technology Kottayam
B.Tech, Computer Science and Engineering • Kottayam, Kerala, India • 2020 – 2024
Narayana Junior College
MPC • 2018 – 2020
Sri Chaitanya E.M High School
SSC • 2017 – 2018
Technical Skills
Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Scikit-learn,XGBoost,Matplotlib,Seaborn
Databases: SQL,MySQL,SQLite
Data and Analytics: Tableau,Power BI,Data Cleaning,Exploratory Data Analysis,Feature Engineering,Model Evaluation
Tools and Methodologies: Git,GitHub,VS Code,Jupyter Notebook,Google Colab,MS Excel
Projects
Food Dataset — Data Cleaning & Exploratory Analysis
Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Exploratory Data Analysis
- Cleaned and preprocessed a real-world food dataset by identifying and handling missing values, duplicates, and outliers to improve downstream analysis quality.
- Applied numerical imputation (mean/median) and categorical imputation (mode) to restore data integrity while documenting assumptions and imputation impact.
- Performed exploratory data analysis using summary statistics, correlation analysis and visualizations in Matplotlib and Seaborn to reveal key patterns and relationships.
- Produced a cleaned, analysis-ready dataset and visual dashboards to support future machine learning experiments and stakeholder review.
Stock Price Movement Prediction (TSLA)
Tools Used: Python, Pandas, NumPy, Scikit-learn, XGBoost, Feature Engineering, Model Evaluation
- Collected and preprocessed historical Tesla (TSLA) market data, created time-based and price-delta features such as open-close difference and high-low range.
- Engineered additional indicators including quarter-end flags and scaled features to improve model input consistency.
- Implemented and compared classifiers including Logistic Regression, SVM, and XGBoost using train-test split and feature scaling.
- Evaluated models using ROC-AUC and confusion matrix metrics to identify the best-performing classifier for next-day movement prediction.
Certifications
Data Analysis with Python — IBM (Coursera)
Introduction to Artificial Intelligence — IBM SkillsBuild
Data Analytics and Visualization — Accenture North America (Forage)
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