Ayush Gaur has extensive experience and skills in big data analytics, cloud computing, and data science. He holds an M.S. in Computer Science with a concentration in data science from UT Dallas and a B.E. in Computer Science from Chitkara University in India. He has professional experience as an instructor for big data and analytics and as a senior associate focusing on big data, analytics, and cloud computing at Infosys. He has strong technical skills in Apache Spark, Hadoop, Python, and cloud platforms like AWS.

1. Ayush Gaur
469.951.9343; ayush.gaur@utdallas.edu
http://www.linkedin.com/in/ayushgaur
EDUCATION
The University of Texas at Dallas May 2017
M.S., Computer Science, Data Science GPA 4.0
Chitkara University, HP, India August 2012
B.E., Computer Science GPA 3.6
CERTIFICATIONS & TECHNICAL SKILLS
Certifications R.H.C.E, AWS Certified Solution Architect – Associate Level, Splunk Certified Knowledge Manager, IBM
Big Data Fundamentals Technical Proficiency, IBM Private Cloud Solution Advisor v3
Analysis Tools Apache Spark, Apache Hadoop Ecosystem and Tableau
Programming MapReduce, Apache Spark with Scala, Python, NoSQL (MongoDB, Cassandara and HBase), SQL,
JAVA, J2EE
Database HP Vertica, MongoDB, Cassandra, Hbase, Ne04J
Others Amazon Web Services (EMR, EC2, DynamoDB, RedShift, VPC and Kinesis), OpenStack, CloudStack
PROFESSIONAL SUMMARY
 Instructor, Big Data and Analytics Club at UT Dallas, developed competency among 250+ students and 50+ Industry
professionals in area of Big Data and Cloud Computing.
 Technology evangelist and a former Senior Associate at Big Data and Analytics vertical of Education and Research, Infosys
Limited, currently pursuing Masters in Sciences in Data Science Concentration at University of Texas at Dallas.
 Over 3 year of experience in Data Analytics, technical training, Project development and design for various business
verticals.
 Proven track record as Researcher, consultant and educator. Emphasis towards quick and effective development achieving
increased productivity using data analysis and mitigating risks using predictive risk techniques.
 Industry Certified technical skills and communication skills, Core Strengths - Big Data Analysis using various distributed
frameworks and Cloud Computing.
 SME in real time, batch data analytics, performing predictive analytics and data mining along with concise data
visualization.
BUSINESS EXPERIENCE
Infosys, Bangalore, India January 2012 – July 2015
Senior Associate, Big Data & Analytics
 Offered consultancy services to production units related to client’s Cloud and Big Data requirements.
 Improved efficiency of the multiple projects by developing POC’s in line with the client’s requirements.
 Improved efficiency of R&D Vertical by 12.6% by shifting computing model to hybrid cloud and analyzing the exact need of
the resources.
 Configured 50+ node Hadoop and Spark Cluster for client POC creation.
 Developed competency among fellow employees in the latest technologies related to Big Data Analysis and Cloud
Computing esp. Public Cloud, successfully delivered more than 150+ session compassing more than 8 courses and training
around 600+ employees with a healthy feedback of 4.6 on a scale of 5.
PUBLICATIONS
 Implementation framework for Cloud based Education-as-a-Service in 2nd IEEE Conference on MOOCs
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7020241
(INSPEC Accession Number: 14881439)
 Ready Reckoner for Quick Cloud Adoption in International Journal for Research in technological Studies (IJRTS)
http://www.ijrts.com/Downloads1.php?file=140110003 (ISSN (online): 2348-1439)
INDUSTRY PROJECTS
Predicting Software Vulnerabilities from NVD (National Vulnerability Dataset) (September 2015 - Current)
 Data cleansing, refining, extracting and creating new data dimensions (e.g. disclosed_date)
 Applying classification and regression in order to predict the best method for TTNV (Time To Next Vulnerability)
Predictive Maintenance (February 2015 to July 2015)
 Developed predictive and descriptive models using Hadoop, Hive, HBase, Spark & R by implementing various
techniques such as Spearman’sRank, KNN and K-mean algorithmsin order to decrease reboot cycles and WLAN errors
and increase WLAN Signal quality on enterprise network routers
 Visualized the co-relation using R between various variables affecting the router’s performance
 Achieved 19.3% less Reboot Cycles and 15.7% less WLAN errors