Tharun Peddisetty

470-***-**** adegsp@r.postjobfree.com LinkedIn GitHub GitHub Gist Medium EDUCATION

The University of Texas at Dallas M.S., Business Analytics [Track: Data Science] GPA 3.8 - May 2021 Lovely Professional University, Punjab, India B.Tech., Electronics and Communication (Hons) GPA 4 - July 2018 CERTIFICATIONS & TECHNICAL SKILLS

Certifications: Python for Data Sciences (Udemy), AWS Data Analytics Fundamentals, Tableau Fundamentals Analysis Tools: Tableau, PowerBI, ArcGIS Business Analyst, Qlik view, Gephi, MS-Excel, D3 Programming: Python, R, SAS, STATA, NoSQL, Shell Scripting, MATLAB, Java, C++, C#, C, html Big Data Ecosystems: Spark, Hadoop, MapReduce, HDFS, Hue, Hive, Sqoop, Flume, Pig, AWS, Databricks Databases: MySQL, Microsoft SQL Server, MongoDB, Oracle, Amazon S3 BUSINESS EXPERIENCE

Infosys, Systems Engineer, India February 2019 – August 2019

• Extracted huge volumes of client’s online order details from Database using SQL queries to perform data analysis for reporting, maintaining vendor audits and ensured compliance.

• Implemented regression models to analyze web-application software testing data to predict test latency and proposed an excel-based test case framework that reduced test latency by 30%.

• Performed data quality checks and sent client recommendations to mitigate issues. Executed data cleaning using Python and Tableau for exploratory analysis. R had been used for descriptive and inferential analytics.

• Achieved 92% in generic training which included competitive python and SQL programming. Bosch and Siemens Home Appliances Group, Research Intern, India June 2017 – June 2018

• Conducted market analysis using Web Scraping in Python about competitor products, their features and price in order to implement clustering, polynomial regression and create dashboards on Tableau.

• Generated MBOMs and presented analytical reports to team by working in synergy with other departments.

• Achieved 17.5 Euros cost reduction in component costs by analyzing reverse engineered product data.

• Lead a team of 4 that was responsible to create an IoT based wireless module making the Refrigerators smart. The IoT module was built just in under 2 Euros. [Patent Published]

• First intern to deliver a keynote presentation regarding the IoT project and won many accolades. ACADEMIC PROJECT

Tennis Ball Detector using Computer Vision in AWS June 2020 – July 2020

• Implemented convolutional neural networks (CNN) that detects only tennis balls in the images in AWS Sage maker notebook instance. Used Gluoncv library that runs on Apache MXNet DL framework as backbone. Deployed MobileNet1.0 pre-trained model to detect tennis balls in the images which reduced the training time by 50%. Predicting Customer Churn Rate of a Credit Card Company using ANN May 2020 – June 2020

• Used DL framework TensorFlow (keras) in Python to develop artificial neural network (ANN) layers and rightly predicted the customer retention probability with an accuracy of about 86% Cats Vs Dogs Prediction using CNN May 2020 – June 2020

• Built all CNN layers from scratch. Trained the model with 10,000 images of cats and dogs and classified new images into right classes with around 99% accuracy. Used TensorFlow deep learning framework and Keras APIs. Computer Vision on AWS Sage maker May 2020 – June 2020

• Implemented Image Classification, Object Detection and Image Segmentation using MXNet with pre-trained models from Gluoncv Model Zoo on AWS Sage maker. Improved execution time by 50% compared to using TensorFlow in python. Marketing Analytics Consulting Project for Sterling-Rice Group (CO, USA) April 2020 – May 2020

• Classified customers into wellness categories. Performed statistical analysis and determined the features that best reflect a healthy product. Based on the results, a new product was recommended to the client. Executed inferential analytics to suggest strong marketing strategies to boost the new product sales. Database Management using SQL and NoSQL – Wildfires in US from 1992-2015 October 2019 – December 2019

• Performed pre-processing and explanatory analysis on SQLite dataset (2 million rows), transferred the data into MySQL and NoSQL databases and drew insights by analysis using queries as per the business need. Validated the results in MongoDB.