Riddhi Laxmikant Patil
M.S. in Business Analytics, University of Illinois, Chicago, IL Dec 2019
B.E. in Information Technology, University of Mumbai, Mumbai, India July 2017
Python, R, SQL, PL/SQL.
Business Intelligence Tools:
Tableau, AWS, Microsoft Word, Excel, Visio, Jira, Confluence, ALM.
Linear and Logistic Regression, Decision Trees, Random Forest, K-NN, Naïve Bayes, Support Vector Machines, Gradient Boosting, Dimensionality Reduction, Clustering, Principal Component Analysis
Microsoft SQL Server, Oracle, Teradata, NetSuite
Reading in Motion, Chicago, IL, United States Aug 2019 – Dec 2019
Data Science Intern – Capstone Project
Instrumental in creating a data warehouse to gather pre-processed and cleaned data to generate insights about the students.
Worked on analysing the effectiveness of client’s program, thereby providing the Business Intelligence team with better insights. Proposed changes to the program based on the analysis performed.
Built and maintained SQL scripts, indexes, and complex queries for data analysis and extraction from the data warehouse as per business user requirement.
Created Interactive dashboards using Tableau to understand the performance of student and coach level data.
Studied Hypothesis testing in the absence of a control group to understand whether the performance of the student was significant at the Beginning of the Year vs End of year using Mixed Effects Second Order Modelling using R.
Awarded the First Prize at the UIC Project Expo for the exceptional project that was implemented.
InfoStretch Corporation, Santa Clara, CA, United States June 2019 – Aug 2019
Data Analyst Intern
Developed a data collection and extraction framework for the purpose of testing client’s heart rate monitoring device against a gold standard device on Human Subject Validation Project for Google.
Identified the top 200 fast moving drugs by designing a POC solution for Kaiser’s pharmacy data using Python and its libraries.
Worked on Inventory Optimization for the client by implementing ARIMA model to analyze the multi variate time series data for trends, seasonal effects and environmental conditions to forecast future demand with an accuracy of 98%.
The proposed model helped in identifying reorder quantity, trend analysis and refilling prescriptions for the client.
JP Morgan Services India Private Limited, Mumbai, MH, India Feb 2018 – July 2018
Performed querying and mining of large datasets to discover patterns using predictive and statistical analysis.
Identified data issues and provided recommendations for resolving these issues to ensure optimal performance.
Developed test plan and cases by analysing business requirements, highlighted multiple defects for various functionalities.
Indian Railways, Mumbai, MH, India June 2016 – July 2016
Data Engineer Intern
Salary Analysis of Chicago Police Data (Tools: Python, Microsoft Excel)
Developed a statistical model using hypotheses analysis and linear regression on a dataset containing 212,058 observations with 19 variables to find the factors that affect the annual salary of the employees of the Chicago Police Department.
Used Welch Two Sample T-Test and ANOVA test to measure the statistical significance of our hypothesis of the factors influencing the salary. variance in our dependent variable.
Delta Airlines Arrival and Departure Analysis using Data Visualization Tools (Tools: Tableau)
Utilized the 2017 aviation data consisting of 6 million records and created different visualizations to explain the on-time performance of Spirit Airlines such as arrival/departure delays, cancellation analysis, route analysis etc.
Analysed major area of concerns and provided recommendations for process improvement based on KPI comparisons.
Sentiment Analysis on Amazon Fine Food Reviews (Tools: Python, Microsoft Excel)
Performed Sentiment Analysis on the Amazon Fine Food Reviews data to predict whether a review is positive or negative.
Performed data pre-processing by removing stop words, numerical values, punctuations and HTML tags.
Implemented Logistic regression with an accuracy of 89.53%.