DATA ANALYST

RITWIN REDDY KUDUMULA

https://www.linkedin.com/in/ritwin/ 1-201-***-**** *********@*****.***

EDUCATION

Stevens Institute of Technology, Hoboken, New Jersey August 2018 – May 2020 Master’s in Information Systems (Major in Business Intelligence & Analytics) Course Work: Data warehousing & BI, Data Analytics & Machine Learning, Advanced BPM, Big Data Technologies, Financial Decision Making, Managing Emerging IT, Marketing Analytics, IT Strategy, Integrating IS technologies, Designing the Knowledge Organization SRM University, India August 2014 – May 2018

Bachelors of Technology in Software Engineering

Course Work: Agile, Big Data, Cloud Computing, DBMS, ERP, Python, Software Design & Testing, Statistics, Project Management SKILLS

Programming & Data analysis: Python, R, SQL, SAS, C++, Java Data Visualization: Tableau, MS Excel, Minitab, Google Analytics, R Shiny Tools and Technologies: Signavio, UML, AWS, DataBricks, Hadoop, Pyspark Core Competencies: Data Analysis, Data Visualization, Machine Learning, Statistical Analysis, Marketing Analytics, Eliciting Requirements, Structured Analysis, Business Process Re-Engineering, Building Client Relations, QA, ETL EXPERIENCE

Stevens Institute of Technology, Hoboken, New Jersey August 2019 – Present Graduate Research Assistant

• Assisting Hanlon Financial Lab’s primary research works with Shoprite® investigating promotional effectiveness of various marketing efforts by looking at how promotions and discount amounts at different stores are affecting the sales

• Integrating data from Shoprite’s 230 stores across USA in order to draw conclusions for managerial action and strategy

• Performing Cluster Analysis, Conjoint Analysis in SAS and R and building Price Elasticity models and Regression Models to get the promotion to sales ratio and giving recommendations for an improved marketing and pricing strategy Graduate Teaching Assistant

• Teaching assistant for graduate course BT466 Data Analytics. Preparing course material (PPTs & R Scripts) to assist students and teaching activities. Updating course structure each semester by researching current industry requirements

• Devising and running R programming-based lab sessions, assignments, homework and exams

• Syllabus includes: Conditional Process Analysis, Data manipulation, Regression, Multicollinearity, Mediation, Moderation etc. Shivani Enterprise, India January 2017 – June 2018 Data Analyst

• Coordinated with the management to discuss business needs and designed database solutions accordingly

• Created a database and schemas using the raw data from excel, spreadsheets, and text files to ensure effective usage of available data and improvise company's querying and analysis. Developed Stored Procedures, Triggers, and created indexes.

• Analyzed Financial Data of sales, deposits and withdrawals, developed tableau dashboards to generate recommendations, trends, and targets in the market to help strategic planning process, and increase the revenue.

• Developed and optimized clustering models to segment users based on financial and demographic attributes

• Prepared reports that interpret consumer behavior, market opportunities and results, trends and investment levels ACADEMIC PROJECTS

Success rate prediction for Kickstarter project (Big Data - Python & Pyspark)

• Predicted success rate of a Kickstarter project by implementing different machine learning algorithms (Decision trees, Logistic Regression, Random Forest and Gradient Boost Tree) on 1 Million rows of data in Pyspark. Best accuracy obtained is 72%

• Implemented ML model on cloud (Databricks) in Pyspark to decrease run time and make it compatible with real time data Sentiment Analysis on EVE online case (Data Visualization – SQL & Tableau)

• Performed sentiment analysis on 2.4 million rows of data to display tangible results with risk and reward as driving factors

• Constructed decision support measure by developing interactive dashboards that include Box-Plots, Histograms, Heat Maps, Line and Bar graphs and has dynamic connection to the SQL server; Performed Data cleaning and transforming in SQL

• Analyzed user reaction towards every update and recommended features that would increase user engagement Housing Prices Prediction (Advanced Regression Techniques - R)

• Exploratory Data Analysis using ggplot2 package to visualize correlation of features and using random forest to predict features that have the most impact on sales price; Manipulated data to fix all missing values

• Determined housing price using gradient boosting machine achieving 76% accuracy

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