Data science, Machine learning, Java, SQL, C programming, Python.

Email: ******************@*****.***

Phone: 917-***-****

LinkedIn: https://linkedin.com/in/datta-

subrahmanyam-a21ab193/

Y DATTA SUBRAHMANYAM

SUMMARY

• An entry-level Data science and machine learning enthusiast with over 2 years of experience in application development and maintenance, facilitating engineering solutions with a variety of technology skills.

• Experience in design and development of full-stack application for user-centered design.

• Extensive experience in VBA Macros based automation development and report administration for PMO management team using RPA tools.

SKILLS

EXPERIENCE

EDUCATION

M.Tech. CSE

JNTU Kakinada

Kakinada, India / 2018-2020

B.Tech. ECE

K L University

Guntur, India / 2010-2014

NPTEL CERTIFICATION IN

BIG DATA COMPUTING

Feb 2019 – Apr 2019

JAVA, VBA Macros, C programming, Machine Learning, SQL, Python, HTML,CSS APPLICATION DEVELOPMENT SENIOR ASSOCIATE CONSULTANT NTT DATA / Bengaluru, India / AUG 2015 – AUG 2017

• As part of a communications team, I was involved in tasks such as maintaining and troubleshooting the J2EE environment on Jboss server that hosts the IP services application, for achieving 90% operational efficiency.

• Designed and implemented JAVA web application, streamlining the organization level travel requests and vehicle allocation by using a light weighted frameworks like HIBERANTE & JavaScript for reducing the load on the database query traffic and scaling the high load result set catering up to 3- 5times, at the backend and frontend.

• As part of an automation team, I have developed dashboards backed by VBA Macros scripts for PMO process like automated report generation which has made a hectic manual reporting process into an automated 15-minute process.

ACADEMIC PROJECT – LINK PREDICTION IN SIGNED SOCIAL NETWORKS M.Tech - JNTU Kakinada / Kakinada, India / Aug 2019 – Present

• I have been working on prediction of potential links in signed social networks through deep learning based signed latent factor (SLF) model which involves unsupervised learning methods like similarity- based or Likelihood estimation method followed by dimensionality reduction techniques like graph embedding and node feature embedding.

• The above proposed model has to be compared with the “State of art” methods such as Adamic-Adar score, Katz score-based ensemble classifier and proximity-based methods. PROJECTS

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