Thandava Siva Krishna Kolagani
Career Objective
Data Analyst with 0 years of experience delivering data cleaning, exploratory analysis, predictive modeling, and dashboarding using Python, SQL, Power BI, and Tableau. Built an end-to-end telecom churn project that cleaned raw customer data with pandas and NumPy, trained Random Forest and XGBoost models via scikit-learn and xgboost reaching 85.5% accuracy, and published reproducible notebooks in Jupyter and Google Colab. Performed SQL analysis in Snowflake and PostgreSQL to segment customers and extract revenue and churn patterns for retention and sales insights. Designed interactive Power BI and Tableau dashboards to track KPIs and visualize trends for non-technical stakeholders. Comfortable with Snowflake data warehouse, PostgreSQL querying, Excel-based data prep, and version control with Git and GitHub. Skilled at feature engineering, EDA, and translating analysis into actionable recommendations for product and retention teams. Seeking an entry-level Data Analyst role to apply statistical analysis and data storytelling to business problems.
Education
Technical Skills
Projects
- Cleaned and preprocessed telecom customer tables using pandas to impute missing values and encode categorical features for modeling
- Conducted exploratory data analysis with seaborn and Matplotlib to visualize distributions and correlations that guided feature engineering
- Built and tuned Random Forest and XGBoost classification models using scikit-learn and XGBoost, achieving 85.5% accuracy on holdout data
- Performed SQL-based segment and cohort analysis in Snowflake to identify high-risk customer groups and service usage patterns
- Designed interactive Power BI dashboards to surface churn KPIs and segment-level insights for retention stakeholders
- Transformed one year of sales transactions with Excel to create analysis-ready tables and standardized payment method fields
- Analyzed revenue trends and customer behavior using PostgreSQL queries to extract time-series and cohort metrics
- Developed an interactive Tableau dashboard that visualized KPIs, sales trends, and payment method distribution for store managers
- Identified top-performing products and peak sales periods through SQL-based aggregation to inform inventory and promotions
- Published the analysis and supporting notebooks to GitHub and documented reproducible steps in Jupyter Notebook
Certifications
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