SRIVATSAV SHRIKANTH

Analytical & Insightful Data Analyst with over 2 years of experience leveraging Microsoft Excel, PowerBI, and Power Query to create dynamic dashboards and drive quality improvements. Skilled in data visualization, root cause analysis, and KPI tracking to enhance decision-making. Proven ability to deliver actionable insights through data-driven solutions. Currently pursuing a Master's in Applied Data Analytics and a Business Insights and Analytics Certificate, focusing on advanced analytics and business intelligence.

• Professional Project: Led the implementation of the Qsix tool on the assembly floor, a quality management system inspired by Porsche, to reduce rework activities and track KPIs such as Cost of Poor Quality (COPQ) and First-Time Quality (FTQ). Focused on data wrangling and visualization, combining complex datasets from multiple assembly lines using PowerQuery and developing interactive PowerBI dashboards that categorized defects based on quality ranking numbers, ultimately increasing First Time Right (FTR) rates from 91% to 97%.
• Python Project: Developed a command-line Wordle game in Python, focusing on robust class-based architecture and user interaction. The project involved extensive data wrangling and visualization using the colorama module to implement color-coded feedback for guesses, enhancing gameplay clarity. Built efficient methods for validating inputs against a dataset of five-letter words, ensuring word uniqueness and adherence to rules. Designed a flexible and reusable class that incorporates methods for user input validation, result tracking, and visual feedback, with unit tests for key functionalities to ensure program reliability and correctness.
• Analytics Project: London Housing Dataset - This Data set provides information on the different boroughs, their Housing prices, type, size and locations. Analysis has been conducted based on the categorical variable - “price” and attributes that relate and revolve around it. Mean, SD, fivenumsum were calculated and outliers were removed. Correlation between the attributes price and sqft. were observed along with CLT verification and data sampling using SRS and Systematic methods.
• Foundations of Machine Learning Project: Using a dataset from Capital Bikeshare, the study employs statistical methods like multiple linear regression and ANOVA to analyze relationships between weather factors (e.g., temperature, humidity) and bike rentals. Key findings include positive effects of temperature and clear weather on rentals, negative impacts of humidity, and significant seasonal differences. Limitations include model assumptions and missing external factors like public events.

• Improved First Time Right from 91% to 97% over a period of 10 months and received the “Star of the Month” award in Dec’21 from the Country Manager

• Microsoft Certified: Power BI Data Analyst Associate
• Microsoft Certified: Azure Data Fundamentals