YASAMAN PANJEBANDPOUR

779-***-****

ac55z9@r.postjobfree.com

PROFILE

Highly skilled in programming and analyzing data with R

Highly skilled in using Machine Learning such as Cluster Analysis, K-Means, Regression analysis, random forest, SVM (support vector machine) and Classification with Rstudio

Experienced in utilizing statistical methods through Python to mine information from datasets

Expertise in querying data in SQL and NoSQL

Experienced in using SQL through R

Highly skilled in cleaning, preparing and visualizing data

Experienced in using AWS (Amazon Web Services)

Experienced in visualizing data through Tableau

Highly experienced in using different statistical methods for analyzing data

Proficient in strategies of analyzing high dimensional data based on random forest method

Highly skilled in multivariate analysis and different regression modeling for prediction

Conducted reliability analysis for software debugging

Extensive knowledge in simulating data with R and Arena

Experienced in using quality control for monitoring data

Familiar with C# Programming and query languages

Demonstrated expertise in problem solving and building relationships with stakeholders

Creative thinker by using analytical and reasoning techniques

Excellent in presenting and communicating complex ideas

Highly skilled in utilizing SPSS and SAS for analysis of data

Expertise in Microsoft Office (Word, Excel, PowerPoint, Access) EDUCATION

M.S. Statistics and Operations research (Full Scholarship) Southern Illinois University Edwardsville, IL

Expected Conferral: Summer, 2018

Master Project: Software debugging by imperfect reliability model using simulation M.S. Mathematical Statistics (Full Scholarship), Conferred: May 2016 Tarbiat Modares University

B.S. Statistics, 2014

University of Tehran

WORK EXPERIENCE

Yasaman Panjebandpour Page 2

Southern Illinois University Edwardsville (SIUE), Edwardsville, IL 2016-Present Teaching Assistant

Using Minitab to analyze data

Running computer labs

Teaching R programming and the statistical methods Paliz Green World, Iran Mar 2014- Mar2016

Data Analyst

Cleaning data and preparing data sets for analyzing

Analyzing and predicting using Rstudio and Python

Using statistical analysis and inferences

Statistical methods such as Analysis of Variance, Regression analysis, Power analysis

Clustering the customers based on annual purchase and predicting the future pattern PROJECTS

Southern Illinois University Edwardsville (SIUE), Edwardsville, IL Fall 2017 Advanced Multivariate Analysis Project

Analyzing wholesale customers data using multivariate analysis and perform two different approaches of clustering, hierarchical, k-means and using HCPC method by FactoMineR and factoextra packages in R.

Southern Illinois University Edwardsville (SIUE), Edwardsville, IL Fall 2016 Quality Control Project

Analyzing data gathered from Candanedo and Feldheim (2016) using multivariate process monitoring and control by Hoteling T2 control charts method for designing the energy- efficient buildings and facilitating productive environment for the occupants Southern Illinois University Edwardsville (SIUE), Edwardsville, IL Fall 2016 Regression Analysis Project

In this study, multiple linear regression method was applied to find the best model with heating load as response variable and eight other variables as predictors. Regression analysis showed that, finally, the variables Relative compactness, Wall area, Overall height, Glazing area and Glazing area distribution impacted the heating load significantly, and the variables including Surface area, Roof area and Orientation did not affect the heating load to be considered in the model.

Yasaman Panjebandpour Page 3

Tarbiat Modares University, Tehran, Iran Spring 2015 Spatial Statistics Project

Spatial analysis of coal ash data from a mine in Pennsylvania and Kriging for new conditions by R Software

Azad University North Tehran Branch, Tehran, Iran Fall 2013 Public Transportation Quality Project

Analyzing data collected from public transportation system users (urban bus) in Tehran using logistic regression method in terms of assessing features and criteria effective on trip time and public transportation quality

University of Tehran, Tehran, Iran Fall 2012

Predicting Cancer Project

Analyzing data of patients with prostate cancer from Tehran University Hospital by Logistic regression and R Software

RECENT PRESENTATIONS

Panjebandpour.Y. & Jafari. H. (2015). Use of Markov Condition of Causal Inference, 10th seminar of Probability and Stochastic Processes, University of Yazd, Yazd, Iran AWARDS/GRANTS

Elected as one of three members of the University of Tehran for National Olympiad of Statistics, Tarbiat Modares University, Tehran, Iran, 2013

3rd-ranked team among all participants in 14th Iranian Student Statistics Competitions with University of Tehran team, University of Sistan & Balouchestan, Zahedan, Iran, 2013

Elected as one of the three members of the University of Tehran team for the 14th Iranian Student Statistics Competitions, School of Mathematics, Computer Science and Statistics, University of Tehran, Tehran, Iran, 2013

Ranked 34th / ~ 6000 participants in nationwide entrance exam for graduate study in Mathematical Statistics, Iran, 2014

RESEARCH INTEREST

Data Analysis

Data mining

Multivariate Statistics

Bayesian Statistics

Bioinformatics

Spatial Statistics

Statistical Inference

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