Aditya Nair

• Extracted relevant data from various sources using Microsoft Graph API and python scripts
• Implemented data models to translate rough business logic into workable, reusable PySpark code; more suited to automation
• Built 3 multi-level Power BI dashboards tracking AI adoption metrics and productivity outcomes across 500+ employees, enabling executive leadership to make data-driven decisions on Copilot ROI
• Reduced manual work done by 90% by creating a metadata-driven ETL pipeline with SCD type 2 using databricks notebooks for data transformations and azure functions to retrieve copilot data via graph API
• Developed extensive documentation in order to communicate business requirements to stakeholders, with detailed proposals on how to proceed; and to serve as an instruction manual for the finished pipeline 

• Designed and implemented 15+ production ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Databricks, processing 3TB+ daily and supporting analytics for 50K+ end users
• Developed numerous reusable Azure Functions to perform data validation, sanity checks and API endpoints across several pipelines, reducing data incidents by 25%
• Designed multi-cloud data architecture leveraging Snowflake for warehousing, BigQuery for analytics, and CosmosDB for real-time operations, reducing query latency and enabling cross-platform reporting
• Implemented 20+ real-time Power BI dashboards, enabling stakeholders to keep track of key metrics with ease 

This is an interdisciplinary course offered by the Departments of Computer Science and Engineering, Electrical and Computer Engineering, Mathematics, and Statistics within the University’s Colleges of Engineering and Arts and Science, and administered jointly with the Texas A&M Institute of Data Science. It offers a catalogue of courses that offers students a bevy of courses that allow students to pursue any of the multiple specializations that fall under the umbrella of data science. Notable courses completed so far during this course are statistical modelling, database management, machine learning and deep learning.

The degree placed a strong emphasis on problem-solving techniques, hardware, software, computation, design, and implementation of programming languages, with a focus on managerial abilities. The goal of the degree was to teach students how to use software and services to turn raw data into actionable business decisions. Notable courses completed during the course of this degree include data structures and algorithms, object oriented programming, data mining and analysis and artificial intelligence.