Alexander Srioudom

San Diego, CA 858-***-**** ad015i@r.postjobfree.com

Summary

Recently graduated and am looking for an opportunity to work in the industry.

Relevant Projects

Forecasting injuries in various countries

Extracted and manipulated data from the World Health Organization on cause of death statistics in R and Microsoft Excel.

Creating preliminary visualizations of each of the datasets with line plots.

Checking if various datasets need a BoxCox transformation by doing initial comparison between preliminary plots and BoxCox transformation.

Checking original plots and transformed dataset plots against a qqplot to check which plot better resembles a normal distribution.

After deciding on which datasets to use, checking chosen plots of data for stationarity or non-stationarity by checking for trends, seasonality, and variance analysis.

Adopting Box Jenkins Methodology by doing the ACF or autocorrelation function plot and the PACF or partial autocorrelation function plot and analysis of these plots.

Analyzing ACF and PACF plots to select parameters for lag values and autocorrelation. Then check for symmetry in ACF plot which implies stationarity, non-seasonality, and non-trends.

We can now select models based on analysis such as Autoregressive model, Moving Average Model, or Autoregressive Integrated Moving Average Model.

After making initial model selections, we test our model selection against other potential models by using the AIC or Akaike information criterion.

After using AIC to determine best model, we check that the residuals are white noise by the Ljung-Box Test.

Make forecasts and prediction values for each of our selected models for any number of future years.

Heart Transplant Survival Analysis

Extract and manipulate Stanford Heart Transplant Data.

Use of the survival and survminer packages in R.

Create survival objects, surv_fit objects, Kaplan Meier Estimates, and visualizations.

Testing and modeling the effect of different covariates against receiving a transplant or not.

Cox proportional hazards modeling.

AIC forward selection process.

Checking proportional hazards assumptions; Schoenfeld residuals, log-log plots, and analysis for different covariates.

Final model selection, reasoning, analysis.

Analysis of regression coefficients and there effects, hazard ratios, confidence intervals, significance levels.

Translating final survival analysis concepts to peers for mutual understanding.

Skills and Relevant Coursework

Programming Languages: R/RStudio/RMarkdown, SAS, C/C++, Python, NumPy, Jupyter, Java; Microsoft Word, Excel, PowerPoint; Latex

Time series, Forecasting, Survival analysis, Regression analysis

Relevant Coursework: Probability and Statistics, Principles of Data Science in R, Design and Analysis of Experiments, Regression Analysis, SAS Programming, Applied Stochastic Processes, Introduction to Mathematical Finance, Mathematics of Fixed Income Markets, Time Series, Survival Analysis

Education

Bachelor of Science in Statistics and Data Science

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