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Data Analyst/Data Scientist/ Business Analyst

Dallas, Texas, United States
January 25, 2018

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Menaka Katapadi Kamath

(469) ***-****


The University of Texas at Dallas December 2017

M.S., Information Technology & Management, Business Intelligence GPA 3.58

Ramaiah Institute of Technology June 2014

B.S., Information Science and Engineering GPA 3.7


Certifications: Graduate Certificate in Data Mining and Business Intelligence by SAS

Google Analytics Individual Certification, R Programming A-Z by Udemy

Machine Learning A-Z by Udemy, Python A-Z by Udemy

Languages: T-SQL, SAS 9.4, R, Python, PL/SQL, C#

Data Visualization tools: Microsoft Excel, Tableau, SAS Enterprise Miner, Power BI

Operating systems: Linux, Windows

Databases: MySQL, MongoDB, MS SQL Server

Big Data Technologies: Hadoop, Hive, Pig, MapReduce, Sqoop


Robert Bosch Engineering and Business Solutions Private Limited, Bangalore, India

Associate September 2014 - April 2015 • Applied Python Pandas, Numpy packages and various statistical methods to analyze over 5 million customer records with 25,000+ basic attributes to explore data distribution, numerical relation and identify feature variables

• Visualized sample data using matplotlib, ggplot, seaborn and other graphing tools to better present the relationship and patterns and enhance comprehension

• Performed data preprocessing, and feature engineering to impute missing value, fix outliers, and make necessary transformations such as categorical one-hot-encoding, numerical standardization and polynomial transformation

• Implemented advanced machine learning models such as Linear Regression, SVM, KNN, XGBoost, Decision Tree, Bayesian inference to train sample data and find optimal solutions through error analysis, hyper-parameter adjustment, model validation techniques, and model ensemble

• Made predictions for different customer IDs, interpreted meaningful insights and communicated analytical reasoning to business partners through dashboards


Data analysis, Visualization & Modelling – Python June 2017 – July 2017

• Imputed missing values, identified outliers using Pandas, Numpy in a data set with 280,000+ rows and 31 columns

• Implemented RF and gradient boosting for feature selection with Scikit-learn; enhancing performance by 10%

• Implemented machine learning models (logistic regression, XGboost) with Python Scikit-learn

• Leveraged Tableau to facilitate Data Visualization expediting analysis for stakeholders.

Analysis of Dallas Crime – Big data Project April 2017 – May 2017

• Processed data into HDFS and analyzed the data using Hive, Pig to get the summary results from Hadoop.

• Handled importing & exporting of large data sets from various data sources into HDFS and vice-versa using Sqoop, performed transformations using Hive and loaded data into HDFS.

• Analyzed the data by designing Hive queries and running Pig scripts to study the insights of the data set

• Synchronized log data from 5 log servers to collect, aggregate data using Flume

Churn Analysis – Advanced R Project Jan 2017 – Apr 2017

• Applied various supervised/unsupervised learning methods such as Decision Tree, SVM, Naïve Bayesian, KNN, Logistic Regression, Neural Network, Bagging, Random Forest, Boosting, and k-Means in R for data analysis

• Evaluated performance (ROC/AUC) of the classification models; optimized and tuned it across parameters.

Customer Analytics Model – Base SAS September 2016 – October 2016

• The dataset records customer purchases at two competitors, and BARNES & NOBLE (B&N) along with customer demographic variables such as education, household size, income, and race

• Built Poisson Regression (count models) to fit the panel data of book purchases at Barnes and Noble at the individual level using MLE and made predictions at population level

• Built NBD Regression Model for accounting unobserved heterogeneity in individual purchase behavior and identified customer characteristics that differentiate purchase of books at Barnes and noble from Amazon

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