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Data Analyst with less than a year in Python, SQL, and Machine Learning
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Data Analyst | B.Tech CSE, CMR University (May 2026) | Python SQL Power BI Tableau Experienced in EDA, Machine Learning, and Predictive Modeling (Scikit-learn, Pandas). Skilled in building ETL pipelines, KPI dashboards, and BI reports that drive business decisions.
CMR University
B.Tech · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Sri Chaitanya Junior College
Intermediate
N/A – May 31, 2022
Sri Chaitanya EM High School
SSC
N/A – May 31, 2020
Bitcoin (BTC) Market Analysis & Price Forecasting
June 23, 2026 – Present
Collected and preprocessed 3+ years of BTC historical OHLCV data via API and CSV ingestion using Python, reducing data inconsistencies by 90% through null handling and outlier removal. Performed EDA to identify BTC price volatility, volume trends, and bull/bear market cycle patterns. Engineered 4 technical indicators (SMA, EMA, RSI, Bollinger Bands), improving buy/sell signal detection accuracy by 22% over baseline moving-average strategy. Built predictive model to forecast short-term BTC price direction using historical OHLCV data. Developed 5+ interactive visualizations (candlestick charts, volume plots, correlation heatmaps) using Matplotlib and Seaborn, enabling pattern identification across 3+ years of price data.
Customer Churn Prediction & Retention Analytics Dashboard
June 23, 2026 – Present
Analyzed 10,000+ customer records using Python (Pandas, NumPy) to identify key churn drivers via EDA. Built Random Forest & Logistic Regression models achieving 85%+ accuracy using Scikit-learn and SMOTE. Queried and transformed 10,000+ customer records using advanced SQL (CTES, Window Functions, JOINs), cutting data preparation time by 35% for the predictive pipeline. Designed interactive Power BI dashboard tracking churn rate, CLV, and MRR at risk across customer segments. Recommended retention strategies reducing estimated churn by 15% based on contract-type analysis.
Financial Performance Analysis & KPI Dashboard
June 23, 2026 – Present
Extracted and consolidated 5+ years of financial data across 10+ revenue and expense tables using SQL (CTES, Window Functions), enabling unified cross-functional reporting. Performed YoY growth, trend, and variance analysis using Python (Pandas, Seaborn, Matplotlib), surfacing 3 under-performing quarters that drove budget reallocation decisions. Built Tableau KPI dashboard tracking 6 financial metrics (gross margin, EBITDA, operating profit) across 4 regions, reducing manual reporting time by 30%. Automated data cleaning pipeline in Python, reducing manual reporting effort by 40%. Identified underperforming cost centers contributing to 18% excess operational spend.
Python Skill Up
GeeksforGeeks
June 1, 2026 – Present
Machine Learning
Simplilearn
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of skills relevant to data analysis, including financial, market, and customer analytics. The use of various tools (Python, SQL, Tableau, Power BI) and techniques (ML, EDA) shows adaptability. However, all projects are academic, limiting insight into real-world collaboration, problem-solving under pressure, or alignment with specific company cultures. The candidate is currently pursuing a B.Tech, indicating a junior profile.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on structured problems, apply analytical techniques, and present findings through visualizations. The academic nature of projects suggests a learning-oriented individual. However, without professional experience or psychometric test results, it's difficult to assess stress handling, team collaboration, or broader operational fit.