SQL, R, Tableau, Tableau Prep, Python (Basics)

SEJAL SHAH

linkedin.com/in/sejal-**a ac949w@r.postjobfree.com 609-***-**** Folsom, California - 95630 github.com/Sejal2396

•A very good team player with excellent writing, communication, presentation and interpersonal skills with the ability to work independently and ready to accept challenges and learn new technologies.

EDUCATION

Master of Information Technology and Analytics (GPA 3.97/4.0) Expected Dec ‘19

Rutgers – The state university of New jersey, New Brunswick

•Experience in Artificial Business intelligence, Business Forecasting, Business data management, Data Visualizaion and Data analysis & Decision making.

Bachelor of Engineering, Electronics and Communication Engineering (GPA 7.56/10) June ‘17

Indus University, India

•Hands on experience in Python, SQL and HTML.

SKILLS

•Data Warehouse/BI Tableau, Tableau Prep, MS Project

•Language/Tools R, python, HTML

•Database SQL Server, MySQL, Oracle database, MS Access

PROFESSIONAL EXPERIENCE

WEX May’19 - Aug’19

Business Strategy Analyst Intern Maine, USA

•Work with management and different team members to identify and gather the business needs, challenges and opportunities.

•Perform business analysis and reporting and develop strategies to meet these needs.

•Build Strategic models to recommend new techniques and technologies to achieve business goals.

Scoring Model

Premium Fleet Services (PFS) department

•Fetched the needed data using Business Objects like Trifacta tool, SAS, etc for further analysis and reporting.

•Segmented the data (~10,000,00 obs, 26 variables) with the customers having single and more than one product, assigned scores to each variable based on its weight using R, Tableau, Tableau prep, SQL

•Presented the report to the Marketing department to further decide the process of cross-selling and campaigning to attract consumers to buy additional products based on relevant scores obtained from the model.

Telematic Penetration Project

Telematics department

•Cleansed and Analysed the dataset (4868 obs, 14 variables) of all the customers having Telematics product, fetched the useful information by writing queries.

•Compared/Combined the datasets using R, Tableau and Tableau Prep to uncover future opportunities.

•Identified 10,000 vehicles versus 4868 telematic units and presented the report with targeted accounts to increase the consumption of WEX telematic products.

ACADEMIC PROJECTS

Prediction of Heart Disease using Logistic Regression with R

Analysed and visualized data using AIC, R square and deviance residual methods.

Got 78% prediction accuracy on the test data. Compared results with results of a Naive Bayes implementation.

Processed and cleaned the UCI heart disease dataset of 14 attributes to train a logistic regression model.

Business Forecasting with R

Performed Average, Naïve, Seasonal Naïve, Regression, STL, Moving Averages and Arima models on bivariate series.

Forecasted that the employment of Retail trade jobs will increase for the next 12 months (Dec’18 – Nov’19).

The dataset includes the monthly nonfarm payroll data from the employment report issued by the Bureau of Labour Statistics

Machine Learning with Python and R

Trained and tested an SVM model to classify 2-class images, with a precision of 0.84.

Performed data visualization of this classifying algorithm and compared results with a Logistic Regression model.

The dataset consists of 25k images of dogs and cats in which the categorical value assigned for Dog is 1 and for Cat is 0.

Regression analysis with R and Tableau

Used Multiple Regression analysis to see the effect of Critic score based on Video Game Sales of Japan, Europe and America.

Cleaned the data of the Video game Sales dataset in R that consists of 15 attributes and 17000 observations.

Conducted analysis in tableau to find the reason behind no relationship between them.

Time Series prediction with R

Finely tuned parameters to predict the number of available parking spots, at a given day and hour with 72% accuracy.

Developed a time series prediction algorithm using Neural Networks and Arima Models.

Conducted 25+ training patterns for neural networks and forecasted availability of parking spots for next 48 hrs.

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