Data Analyst Engineering

Tharun Krishna Nirmala Singaravel

**** * ***** *** ***** Drive, #2009 S, Tempe, Arizona - 85281.

********@***.*** 480-***-**** https://www.linkedin.com/in/tharun-krishna SUMMARY:

Current Graduate student pursuing Masters in Industrial Engineering with a specialization in Industrial Statistics and Data Analytics. Over 1.5 years of Research and Industrial experience in Data Analysis with an ability to provide strategic solutions and business ideas. EDUCATION:

Master of Science, Industrial Engineering (CGPA-3.42/4.0) May 2018 Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona Bachelor of Engineering, Mechanical Engineering (CGPA- 7.8/10.0) Apr 2016 Sri Venkateswara college of Engineering, Chennai, India RELATED COURSEWORK:

Design of Experiments, Regression Analysis, Data Mining, Data Science for System Informatics, Information Systems Engineering, Computational Statistics, Production Systems, Applied Deterministic Operations Research, Advanced Quality Control WORK EXPERIENCE:

Graduate Data Science Research Assistant, Arizona State University, Tempe, Arizona Sep 2017 – Present

• Used Natural Language Processing (NLP) to discover ways to improve energy effectiveness in U.S Navy’s Operations by analyzing the text extracted from over 50,000 Licensee Event and Inspection Reports of 99 nuclear power reactors in the U.S.

• Developed predictive models using machine learning algorithms with the help of NLTK and Scikit-Learn libraries in Python to forecast the class label of the reports. Built topic models using Latent-Dirichlet Allocation (LDA) to derive hidden patterns.

• Developed a Term Frequency – Inverse Document Frequency (TF-IDF) model to examine the uniqueness of each reactor in terms of difficulties and issues faced. Used Regular Expressions for analyzing and extracting information out of the text corpus.

• Built networks from texts using bi-gram allocations for a given window size and calculated T-score and PMI. Data Analyst Intern, Metal Scope India Private Limited, Puducherry, India Jul 2015 – Jun 2016

• Collected data from various departments, improved data quality, created a database server and built SQL queries in MySQL and Microsoft’s SQL server platform.

• Generated Ad-hoc reports through SSRS and Power BI to keep track of the long-term goals and make effective marketing and sales decisions. Performed A/B and multivariate tests to identify KPIs before implementing a business strategy.

• Carried out descriptive statistical data analysis through Excel with the help of pivot tables and R to give reports on the various defects faced in the construction of Pre-Engineered Building and the measures taken to overcome it.

• Conducted Root - Cause Analysis and created flow charts, graphs to improve organizational efficiencies.

• Built predictive models to estimate the cost of new builds and renovations up to 20 months before the project breaks ground. Created dashboard in Tableau for stakeholders to make important financial and business decisions. ACADEMIC PROJECTS:

Supply Chain Decision Support System Spring 17

• Designed a Decision support system for the supply chain division of a food chain company using SQL database and VB.NET.

• Provided a strategic solution in terms of the location of production center for each outlet and decreased the material handling and transportation costs by 20%. Performed detailed analysis on the performance of every outlet and ranked them suitably. Regression Analysis on Antral Follicle Counts due to various factors Spring 17

• Developed a regression model in R to analyze the factors responsible for successful pregnancy (antral follicle count).

• An initial model with all the regressors was built. Residual analysis, transformation to meet model adequacy, examination of multicollinearity was performed. Different model selection methods were applied to get the final model with 91% accuracy. Sentiment Analysis on Amazon Product Review Data using Natural Language Processing Fall 2017

• Gathered customer reviews of electronic and clothing goods sold on Amazon to discover solutions for better customer service. After extracting the sentiment sentences, NLTK was used for tokenizing, stemming, and POS tagging the data.

• Categorized the reviews into positive, negative, and neutral using classifiers such as Random forest, SVM and Naive Bayes.

• Performance of each classifier was estimated based on its F1-score and ROC (Receiving Operating Characteristic curve). Analysis of factors affecting the comfort level in Bikes (Design of Experiments) Fall 2016

• Designed a 24 factorial model with center points to optimize the settings of a bike for maximum comfort for the rider.

• Selection of Response variable, checking for model adequacy, identification of major factors and levels for these factors was performed. Formulated a Regression model to predict the response value for different possible values of the factors.

• Performed Statistical Analysis on Minitab to find the effects of each factor and interaction by generating a response surface. SOFTWARE SKILLS:

Data Analytics and Tools: Python, R, Minitab, JMP, SPSS, Weka, Hadoop, Spark, Microsoft Excel, VBA, AMPL, Java, C/C++ Database Management: Microsoft Access, MySQL, Teradata, Microsoft’s SQL server

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