Python, R, Tableau, Machine Learning Algorithms, Data framework

Gogul Vengatesh Krishnamoorthi

adbbuh@r.postjobfree.com https://www.linkedin.com/in/gogul-vengatesh/ +1-469-***-****

EDUCATION

University of Texas at Dallas, M.S Business Analytics (Dean’s Excellence Scholarship), TX - GPA 3.84/4 May 2021

National Institute of Technology Trichy, B.Tech in Electrical and Electronics Engineering, India – GPA 3.45/4 May 2017

TECHNICAL SKILLS

Languages: Python (Pandas, Numpy, Seaborn, scikit-learn, keras, matplotlib), R, C, C++

Database and Visualization: SQL, NoSQL (MongoDB), Tableau, Qlik View, Excel VBA

Tools: Git, MS Excel, ROBOT, Robotic Process Automation (RPA), AWS, Azure, Wireshark, JIRA (AGILE Methodology), Jenkins

Algorithms: Random Forest, Decision trees, K-means Clustering, Association Rules, Collaborative Filtering, Boosting, Regression

Machine learning, Hadoop, power BI, A/B Testing, CHAID, SVM, ETL

PROFESSIONAL EXPERIENCE

Technical Consultant at Radisys, India Tools Used - Python, SQL, Git, GRPC, AGILE, Jenkins July 2017-June 2019

Designed a data warehouse framework to capture data flow from cellular devices to understand customer requirements.

Created an automated pipeline to extract data from 2000 customers and visualize the effectiveness of service for a 4G LTE Network solutions provider using python.

Designed dynamic and highly scalable interactive dashboards for a leading Mobile technology Corporation to compute real time reports and insights on performance indicators. Led to 12% increase in feature enhancements by analyzing the data insights.

Performed trend analysis on channel quality which led to 8% more effective utilization of bandwidth and resources. Developed a cellular handset metric master list to consolidate data and ran SQL queries to extract data for analysis reports.

Business Development Intern at Planezy, India Tools Used - MS Excel May 2016-July 2016

Achieved a 20% rise in vendor activity on company platform by providing a competitive analysis to customer preferences for vendors.

Implemented feedback system to study customer satisfaction and improved vendors’ business strategy using purchase trends.

Maintained database for vendor objectives and provided business solutions through brand development.

PROJECTS

Atos Comparative analysis of Technologies in Companies Python, NLP, Feature Engineering

Extracted data services and solutions used by Atos Competitors from various sources by web scraping in Python.

Used NLP techniques to analyze text data and presented a comparison report on company capabilities and differentiating aspects in AI services areas across healthcare, communications and technology verticals to identify trends for improvement.

Generated word clouds and performed topic modeling to derive vital insights related to AI services, to help competition organizers gain competitive advantage in the industry (Winner of the Competition with over 40 teams participating).

Academic Project on Wildfire Data Analysis MySQL, MongoDb, Python

Extracted and cleaned 1.88 million records of US Wildfires data from SQLite and imported into SQL using python scripts.

Designed relational database model (ER) by transforming unstructured data into various tables with 3NF normalization. Achieved a 10% performance improvement by creating complex queries, views and mongo aggregations to manipulate the data.

Performed exploratory data analysis and cleaned data and joined multiple tables using SQL joins and subqueries.

Virtual Challenge for ANZ Bank R, Tableau

Created interactive Tableau dashboards to analyze transactional information on age, customer volume and spending average. Gathered insights on location specific spending rates, transaction behavior and account related trends

Cleaned data using R (dplyr, ggplot) by analyzing outliers and imputation techniques. Performed correlation analysis among income, age, transaction, location, date, merchant etc. to find the best predictor subset impacting the purchasing behavior of customers

Optimized a decision tree model with 92% accuracy to predict the annual salary for each customer. This enabled to segment customers into income brackets according to purchasing behavior.

Global Health Predictors Python, Tableau

Performed exploratory data analysis and cleaned data with 1 million records to extract indicator data based on its correlation using quantitative methods and used modelling to analyze the data using matplotlib library.

Achieved 8% increase in the predictive accuracy of a Regression model to predict the global health expenditure over HIV spread parameters. Obtained time series visualizations on variation between Government spending and HIV control using Tableau.

ACHIEVEMENTS & LEADERSHIP

1st Position in ATOS case competition, A market research analysis for technology consulting company ATOS

Finalists in Hackoverflow – The Data Visualization Challenge conducted by University of Texas at Dallas

Founding member and Treasurer of AIMDB (Analytics on In-Member Database) club at University of Texas at Dallas

Member and Volunteer of the Data Science Club at University of Texas at Dallas

Headed a 120-member team to organize the overall proceedings for Festember, NIT’s national level cultural festival

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