Data Analyst

Sandra George

EDUCATION

M.S. Engineering Management May 2019

University of Massachusetts, Lowell, Massachusetts B.E. Biomedical Engineering May 2016

SRM University, Chennai, India

TECHNICAL SKILLS

Programming languages: R, MATLAB, SQL

Data analytics tools: Excel, MS Office, IBM-SPSS Modeler, Tableau, Microsoft Azure Machine Learning

, VMachine Learning: Linear Regression, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Tree, Regression Tree

WORK EXPERIENCE

Aldi Inc (Aurora, Illinois) Nov 2019 – May 2020

HR Data Analyst

• Analyzing and evaluating data and reports, feeding back the findings to relevant managers and advising on changes and improvements using Excel and myHR.

• Creates reports or data sets to answer specific inquiries from HR leaderships

• Complete special projects, process improvement projects, and automation projects as needed for the VPs, Presidents and Managers.

Sanford Health (Sioux Falls, South Dakota) Aug 2019 - Nov 2019 Data Analyst Intern

• Applied Predictive modeling technique Linear Regression in R to predict termination of employees with 70% accuracy

• Visualized Actual FTE and Budgeted FTE trend in Excel using Pivot table

• Implemented Machine Learning Model Decision Tree to analyze HR data and recommended reducing the time taken between the interview date and hiring date thereby increasing the recruiting efficiency

• Used HRIS system to create new positions and update data related to HR and non-HR staffs ACADEMIC PROJECTS

Analysis of Absenteeism at Work Jan 2019 – Mar 2019

• Built logistic regression model to predict daily absenteeism for an employer using R studio, achieving a prediction accuracy of 70%

• Analyzed data with Tableau to determine significant predictors in 12-dimensional data, heterogenous dataset of 500 samples

Predictive Analysis of Prudential Life Insurance Oct 2018 – Dec 2018

• Performed data analysis using R studio to make insurance application process quicker and less labor intensive

• Linear regression, Ridge Regression and Lasso Regression were used to accurately classify risk based on the customer data

• Developed visual graphs to understand the data pattern Data Analysis using IBM SPSS Modeler Jan 2018 – Mar 2018

• Used decision tree, neural network, SVM, and logistic regression to detect edible or poisonous mushrooms

• Compared the performance matrices of different models to figure out which model gave the best prediction

• Evaluated what features contributed the most in determining whether a mushroom is edible or poisonous CERTIFICATION

• Python + SQL + Tableau (Udemy) Jan 2019

• Databases and SQL for Data Science (IBM on Coursera) July 2018 addptn@r.postjobfree.com

https://www.linkedin.com/in/sandra-george/

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