Data Project

PRANEETH PATIBANDA

***B South Duncan Street ● Stillwater, OK 74074 ● 469-***-****●abi5p9@r.postjobfree.com

SUMMARY

• Strong coursework in Statistics, Database Marketing and Data Mining.

• Proficiency in handling complex data sets, performing data cleaning, manipulation, detailed analysis and

programming using SAS.

• Ability to perform data analysis that include testing, validating and scoring large data sets using different predictive

modeling techniques and present recommendations to business problems with well written executive summaries.

• Capability to understand and analyze clinical and financial data sets, build predictive models and generate reports.

• Excellent verbal and written communication skills.

• Good team player and ability to work in a cross functional teams.

EDUCATION

Master of Science in Industrial Engineering and Management Expected Dec 2010

Oklahoma State University, Stillwater, OK 74078 GPA: 3.75

Bachelor of Engineering in Mechanical Engineering May 2008

Osmania Universit y, INDIA GPA: 3.5

CERTIFICATIONS

SAS Certified Base Programmer for SAS 9.0 July 2010

SAS Certified Predictive modeler using SAS Enterprise Miner 6.1 May 2010

OSU Data Mining Certificate May 2010

OSU-Lean Training Certificate May 2009

ACADEMIC EXPERIENCE

Data Mining Academic Project February 2010 – May 2010

• Conceptualized and developed a propensity model in identifying prospect customers that would deliver cost savings

for a leading financial firm as a part of live course project.

• Analyzed and selected variables contributing to business needs and screened those variables for missing values,

outliers and irregularities.

• Performed appropriate missing value imputations, variable transformations prior to modeling.

• Evaluated various statistical predictive models such as Regression, Decision Trees, and Neural Networks and

recommended the best model to the client.

• Worked as a part of a team throughout the project and handled team responsibilities.

M 2010 Data Mining Shoot Out June 2010-July 2010

• Performed detailed study on diabetes data set involving 44 variables and over 100000 records.

• Analyzed each variable and screened for missing values, skewness and outliers.

• Implemented data transformation and imputation techniques on the dataset before modeling process.

• Applied various predictive modeling techniques to study diabetes influence on a patient’s total health care expense.

• Documented an executive summary that includes all the results obtained from the modeling approach.

SOFTWARE SKILLS

Languages C, C++, VBA

MS Office Tools Word, Excel, PowerPoint, MS Project, MS Visio, Access

Data Mining Tools SAS Enterprise Guide 4.2, SAS Enterprise Miner 6.1, SAS 9.2

System analysis and Design MS Visio, System Architect

ERP SAP ECC

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