ASHRITHA KANDAKATLA

Data analyst skilled in transforming raw data into actionable insights through statistical analysis, data visualization, and reporting. Proficient in Python, R, SQL, and MATLAB for data manipulation, modeling, and automation. Experienced with libraries such as Pandas, NumPy, Scikit-learn, Seaborn, and PySpark for building data pipelines and performing exploratory analysis. Adept at developing interactive dashboards and visual reports using Tableau, Power BI, Seaborn, and Matplotlib. Strong background in querying and managing large datasets using SQL Server tools, and leveraging Git, Excel, and Access for version control and workflow optimization

Predictive Modeling for Customer Response Classification  

Tools Used: Python, Pandas, scikit-learn, Matplotlib, Seaborn

• Built a machine learning model using supervised learning (Logistic Regression, Decision Trees, Random Forest) to predict term deposit subscriptions from telemarketing campaign data.
• Performed exploratory data analysis and visualized customer behavior, economic indicators, and demographic insights.
• Applied data preprocessing techniques: encoding, feature scaling, outlier treatment, and stratified sampling.
• Achieved high accuracy and model explainability, with actionable recommendations to improve marketing strategy.

ArtBase – Relational Database Design & Implementation

Tools Used: MySQL (T-SQL), PHP, XAMPP

• Designed and normalized a database for managing artist, artwork, customer, and group relationships in a gallery system.
• Implemented SQL schema including foreign keys, stored procedures, triggers, and many-to-many relationship tables.
• Developed a functional web app using PHP for CRUD operations on the “Artists” table with secure form input and validation.
• Demonstrated core DBMS concepts including indexing, joins, and trigger-based updates for customer spending.

Sales & Profit Analysis Dashboard

Tools Used: Tableau

• Analyzed regional sales performance, top-selling product categories, and discount strategies using visual analytics.
• Identified that Technology category and office machines yielded the highest profits; central and southern regions led in revenue.
• Used calculated fields and visualizations (scatter plots, bar charts, maps) to analyze CLV, sales trends, and discount impact.
• Delivered data-driven insights to improve customer targeting, pricing strategies, and regional marketing campaigns.

• Google HubSpot Marketing Certification
• Google Analytics Certification
• PL-300: Microsoft Power BI Data Analyst Associate
• AWS Certified Cloud Practitioner (CLF-C02)