Ishant Aneja
Data Analyst and Python Developer with hands-on experience in data extraction, cleaning, analysis, and visualization using Python, SQL, Pandas, Num Py, and Matplotlib. Skilled in building REST APIs, backend systems, and ETL pipelines with Mongo DB, Postgre SQL, and SQL.
Proficient in MS Excel, Power BI, and Tableau for turning raw data into actionable business insights. Strong foundation in exploratory data analysis (EDA), data manipulation, and dashboard/report development.
Education
MCA
Master of Computer Applications (MCA) Ongoing
K.R. Mangalam University, Gurugram
BCA
Bachelor of Computer Applications (BCA)
Maharshi Dayanand University (MDU), Rohtak
St. Paul Bharati High School
Skills
Data CleaningData ManipulationData ExtractionExploratory Data AnalysisPan-dasMatplotlibPower BITableauMS ExcelSQLPostgreMongoDBGitGitHubGitHub ActionsAlchemyScikit-learnChatGPTClaudeGitHub CopilotREST APIsETL PipelinesData Structures & AlgorithmsMachine Learning Funda-mentalsPython– Programming in– Machine Learning with– Career Edge Program
Projects
Real-Time Stock Market Analytics Pipeline Python, PostgreSQL, SQLAlchemy, Pandas, y Finance, GitHub Actions
– Built an automated ETL pipeline tracking daily price data for 2,392+ NSE-listed tickers via the y Finance API – Designed and integrated a Postgre SQL database using SQLAlchemy to store and query historical stock price data – Automated scheduled data collection using Git Hub Actions, removing the need for manual pipeline runs – Performed data cleaning and transformation with Pandas to prepare datasets for down analytics – Implemented logging and error handling to improve pipeline reliability and simplify debugging Cryptocurrency Risk Prediction & Analytics Python, Scikit-learn, Binance API, Power BIGit Hub – Developed a cryptocurrency risk-analysis pipeline using historical Binance data for BTC, ETH, SOL, XRP, and ADA – Engineered 30+ financial and technical features including RSI, MACD, Bollinger Bands, ATR, ADX, OBV, MFI, volatility, momentum, and lag features – Built a time-series regression model to predict next-day cryptocurrency returns using chronological train-validation-test splitting to prevent data leakage – Evaluated model performance using MAE, RMSE, and R , and performed actual-vs-predicted analysis – Implemented VaR, Parametric VaR, Expected Shortfall, coverage analysis, and strategy backtesting to assess financial risk – Developed four Power BI dashboards covering market overview, technical indicators, model performance, and risk analytics Music Store Sales Analysis Postgre SQL, SQL, Python GitHub – Analyzed the Chinook music retail dataset in Postgre SQL to answer business-driven questions on customer value, genre trends, and city-level revenue – Built advanced SQL queries using window functions and subqueries to rank customers by lifetime value and identify top-performing artists and genres by country – Designed a relational schema and imported multi-table datasets to support data-driven marketing recommendations Skill Exchange Platform Python, Java Script, Mongo DB (MERN Stack) skill-exchange-system. vercel. app – Built a full-stack MERN application supporting real-time interactions using Web Sockets – Designed optimized REST APIs for efficient data handling between client and server – Implemented Mongo DB aggregation queries to improve query performance – Enabled user analytics and activity tracking features for platform engagement insights