The document is a resume for Advait Kulkarni which outlines his education, including a Master's degree in Data Science and Bachelor's degree in Computer Science, as well as relevant coursework. It also details his work experience as a Data Scientist, Data Analyst Intern, and Associate Data Engineer where he utilized tools like Python, SQL, Spark, and Tensorflow. Finally, it lists several of his projects analyzing Spotify music, classifying news articles, and analyzing Chicago taxi trips where he applied machine learning algorithms and data visualization techniques.

1. Advait Kulkarni
Atlanta, GA| advaitan@buffalo.edu | 716-598-8710
linkedin.com/in/advait-kulkarni-1010| https://github.com/advaitoctober
EDUCATION
Master of Science (MS), Data Science, State University of New York at Buffalo Aug 2017 - Feb 2019
Bachelor of Engineering (BE), Computer Science, University of Pune, Pune, India June 2012 – Jun 2016
RELEVANT COURSES
Operations Research, Probability, Numerical Mathematics, Data Models Query Language, Statistical Data Mining,
Data Intensive Computing
WORK EXPERIENCE
Data Scientist, CBD Services | Python, SQL, Spark, Tensorflow (April 2019 - Present )
● Used Python and SQL to transform unstructured data into usable format.
● Used the data to help develop a pricing model based on distinct parameters.
● Worked on developing an end-to-end data pipeline for the client.
Data Analyst Intern, Myngl.io | Python, SQL, Spark (July 2018 – Dec 2018)
● Data preprocessing using MySQL queries where data from distinct files was merged and prepared.
● Generated credit scores for the data based on customers’ utilities, tax and other bills in Python.
● Final product was a data pipeline to process data and generate credit scores.
Associate Data Engineer, AlgoAnalytics| Python, SQL, R, Spark (July 2016 – June 2017)
● Developed a customer attrition model based on their portfolios in Python and R.
● Predicted the brokerage next quarter from the past brokerage data of customers in MySQL, Python and R.
● Used algorithms like random forest, boosting and bagging to predict customer attrition with 91% accuracy.
Software Engineer, GS Labs | Python, Flask (July 2015 – May 2016)
● Developed an OpenStack cloud resource management tool to efficiently allocate virtual space.
● Tool could be used a desktop application instead of a web dashboard.
● Learnt about project management tools like GitHub
PROJECTS
Spotify Music Classification using Clustering Algorithms | Python, R, Regression Algorithms, Web API
● Analysis of experimental classification system of songs included in playlist using audio features extracted from
Spotify & Gracenote’s Web-API.
● Data was transformed using Principal Component Analysis for dimension reduction and fed into Hierarchical
clustering, K-Means, K-Medoids model.
New York Times News classifier using SparkML | Python, PySpark, Regression Algorithms, Beautiful Soup, Docker
● News classifier on articles fetched from NY Times Article Search API and Beautiful Soup.
● Trained Logistic Regression and Naïve Bayes model with accuracy of 91% post cross validation in Spark
Environment.
● The entire project was contained and deployed in a Docker environment.
Chicago Taxi Trip Analysis | Python, MySQL, Regression Algorithms, Pandas, Numpy, Matplotlib, Folium
● Understood taxi traffic flow by community area and duration of day which helped identifying high frequency
taxi routes maximizing driver’s trips.
● Extracted the correlation of trip distance and duration on fare of the trip and built linear regression model
predicting fare for trips with a 92% accuracy.
● Used Folium and Matplotlib to visualize the maps and results respectively.