Charantej Reddy Goli

Phone: 551-***-**** E-mail: acz93g@r.postjobfree.com LinkedIn: https://www.linkedin.com/in/charantejreddy/ SUMMARY

Certified SAS Programmer with 3 years of analytics experience in Business Intelligence, Data Mining, Predictive Modelling, and Decision support systems.

Hands-on Statistical analysis and Data science experience across a variety of business contexts and data sources.

Strong and expert level experience in using R, Python, SQL and Tableau.

Strong Data Visualization skills in communicating the statistical findings to Business users and roll out the Insights into day to day operations.

Equipped with tools and knowledge to gather data from varied sources, clean data for analysis, drive insights from data using statistical methods and deliver results.

CERTIFICATIONS

SAS Certified Base Programmer for SAS 9 by SAS Institute, 2017 – Present, License Number: BP069680v9

The Data Scientist’s Toolbox by Johns Hopkins University on Coursera, 2016 – Present, License Number: LE3C46M2ULD8

Tableau 9 For Data Science on Udemy, 2017 – Present, License Number: UC-3BJOTX2T

Introduction to Data Science in Python by University of Michigan, 2016 – Present, License Number: YZNXHNR98UHH CORE COMPETENCIES INCLUDE

Data Mining Multivariate Statistical Analysis Machine Learning Operations Research Data Extraction & Manipulation Predictive Modeling Web Scraping Exploratory data analysis Data Visualization EDUCATION

Master of Science, Ira A. Fulton Schools of Engineering, Arizona State University. Dec 2016 Bachelor of Technology, National Institute of Technology, Silchar, Assam, India Jun 2012 TECHNICAL SKILLS

Statistical Analysis: Minitab 17, JMP, SAS (Base SAS v9.2), R Studio, Weka for Data mining. Simulation: ARENA, AMPL, MATLAB 2013, CPLEX.

Database: MySQL, Microsoft Access.

Programming: Python 2.7, VBA, XML, HTML.

Lean Tools: KAIZEN, Six-Sigma, Cost Management, Project Management, 5S, Statistical Process Control, Value Stream Mapping Technical Software: VISIO, MS Office Suite, Tableau, Excel, GIT, GITHUB. PROFESSIONAL EXPERIENCE

Data Analyst, HEG Limited Sep 2012 – Nov 2014

Interpreted and translated Business and System Requirements and interacted with users, product owners, and other stakeholders

Worked extensively to generate monthly reports and listings for analyzing the inventory, purchase and budget data

Collected, cleaned and analysed quantitative data related to items and pricing to develop critical insights

Transformed data in various formats (excel, access, CSV) into SAS datasets and developed ad-hoc reports as needed by the manager

Acquired data from multiple sources and performed Data Integration and Data cleaning using R

Compared the trends in different groups using GRAPH Procedures - PROC GCHART, PROC GPLOT

Developed final documentation and reports for review using Reporting Procedures - PROC TABULATE, PROC REPORT and SAS ODS.

Administered business intelligence systems and done business data analysis, visualization and reporting.

Validated the models through residual analysis, R-square value analysis, and normal distribution and so on.

Joining, blending data, data aggregation, table calculations, advanced dashboards and storytelling using Excel.

Wrote SQL queries to update tables and pull data and performed analysis.

Collaborated with Plant Manager to determine data driven reporting needs and tracking measures

Led cross-functional teams and applied lean and six sigma, DMAIC and 5S methodologies to improve process efficiency, reduce material wastage resulting in 6 crores of profit

Collaborated with external customers (Vendors/Suppliers) and internal customers (Finance and sales) regarding the product requirements and indents raised to comply with the budget constraints and to reduce costs

Excelled both as a team player and individual, with excellent communication skills to creatively handle multiple projects with aggressive deadlines and also in training the fresh graduates Graduate Service Assistant, Arizona State University, Tempe, AZ Aug 2016 –Dec 2016

Helped Graduate students from diverse academic backgrounds in the statistical modeling of the data for their research projects.

Helped them to extract data from various types of file formats like CSV, XML, HTML, JSON and even SQL servers for data analysis in R. Assisted them with data cleaning and Manipulation using “dplyr” and “tidyr” R packages.

Used decision trees, Bayesian analysis, Support vector machines and cluster analysis methods for building classification models. Used SVD and PCA for dimension reduction.

Assisted them in building hypothesis testing, ANOVA, classification and regression models using SAS, R, and Python

Classification methods used: Logistic Regression, Decision Trees, SVM, Random Forest, Neural Network (ANN).

Regression methods utilized - Linear, Nonlinear, Boosted Regression Trees, ensemble methods. Analysing over 1.3 million rows of data related to Mortgage Home Loans using Python

Used Numpy and Pandas libraries to perform data munging, data cleaning, and create API with required functions

Used Matplolib, Seaborn and Tkinter libraries to create bar charts and GUI’s to obtain insights about the market metrics

Used sklearn and DBSCAN for geographical clustering to answer questions about home loans market Predictive model building on a synthetic dataset containing 254 features using Python

Pre-processed the data by applying imputation techniques to replace missing values in the data and eliminating redundant features to reduce multicollinearity

Compared predictive accuracy using mean square error metric for models built using linear regression, random forest regression, support vector machine and ridge regression techniques Built a classification model to predict the class labels of a highly imbalanced dataset using Random Forest Classifier in Weka

Built a classification model on the dataset that consisted of 10000 instances, 61 continuous attributes and a nominal target Variable.

Used random forest classifier to build the model with the goal of achieving a balanced error rate.

Data manipulation was done using the smote and resample filters to remove the class imbalance.

Achieved an Out of bag error rate of 8% by the classifier, predicting each class almost equally well. Developed a decision support system with database as well as security and interface usability features

Developed a complete E-R model in an E-R diagram for Management of Healthcare Services.

Transformed the E-R model to a relational model and then implemented it in MS Access and MySQL Database.

Incorporated the relational model in Access into a windows and web application using Visual Studio by writing SQL queries to extract the relevant data.

Added security and interface usability features to windows application by using encryption and decryption concepts. Time Series Analysis and Forecasting of CPI for Medicare data for all urban consumers using R

Analyzed the seasonality and trend of monthly CPI data for 8-year period from 2005-2014.

Examined the time series ACF and PACF plots and modeled the data using seasonal ARIMA method.

Evaluated Residual plots of the above models and concluded ARIMA (0,1,1) (0,1,1) 12 to be the best forecasting model with MAPE of 1.83%.

Discrete Event Simulation Modelling & Analysis of Panda Express Fast Food Restaurant

Recorded and analysed the inter-arrival and service times of a Panda Express restaurant for a particular time interval.

Coded discrete event simulation using Matlab to demonstrate the operation of a queuing system and generated results.

Deployed ARENA to examine the simulation model of the entire system and identified potential bottlenecks.

Proposed a model with improved server utilization, and also reduced the total time spent by the customer by 30%. Regression analysis to find the significant factors affecting the cooling load of a building using R

Collected the data for cooling load in 768 buildings along with the data for the 7 regressors.

Analyzed ANOVA table and used transformations to create an appropriate regression model.

Identified outliers, multicollinearity and re specified the model to mitigate their effects.

Identified the significant regressors and created the appropriate regression model.

Model validation was done using the test data set. Univariate and Multivariate statistical analysis using SAS

Analyzed complex data sets related to psychology, air pollution, healthcare among others using multivariate statistical analysis techniques to produce sound descriptive statistics

Applied the concepts of multivariate regression, principal component analysis, factor analysis, discriminant function, GLM, MANOVA to analyze the SAS outputs to test various hypotheses ACADMIC PROJECTS and DATA CHALLENGES

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