Kyryl
Резюме Data Analyst
8 листопада 2017р.Харківська область
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Професійні навички
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PERSONAL INFORMATION | Kyryl ********* |
(Ukraine) | |
WORK EXPERIENCE |
Dec 2012–Present | Data Analyst |
MicroBilt Corporation, Kharkiv (Ukraine) | |
- Developed and managed products for the alternative financing and short term lending space which reduced default rate by 15% with same approval rate - Developing scripts for data processing and storing which helped to find and stop sending money for multiple times to consumer - Developing automatic transaction processing and storing. |
EDUCATION AND TRAINING |
Sep 2013–Jan 2015 | Master of Science (M.S.), Applied Mathematics | |
National Technical University 'Kharkiv Polytechnic Institute', Kharkiv (Ukraine) | ||
Sep 2009–Jun 2013 | Bachelor of Science (B.S.), Applied Mathematics | |
National Technical University 'Kharkiv Polytechnic Institute', Kharkiv (Ukraine) | ||
PERSONAL SKILLS |
Mother tongue(s) | Ukrainian, Russian |
Other languages | English (CEFR Level – C1, IELTS Band Score: 7 – Good User) |
Communication skills | Good communication skills gained through my experience as ICT Help Desk volunteer during UEFA Euro 2012 |
Organisational / managerial skills |
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Job-related skills | Data Analysis, Statistical Modelling, Data Mining, Data Presenting, Programming, Web scrapping, SQL, SAS, Python, pyspark, Spark SQL, DataFrame API, R, C#, SAS Enterprise Guide, Matlab, Visual Studio, Microsoft Excel, Microsoft Word, XML, MathCAD, WPF, |
ADDITIONAL INFORMATION |
Projects | Cluster and discriminant analysis of Ukraine banks | Produced an analysis of Ukraine bank system. Detect main trends and features that occur in Ukrainian bank market. Built model for predicting bank reliability. Modelling credit data using Logistic Regression | Built a statistic model for predicting credit default. Analysed the performance and accuracy of built system. Key skills: Data Mining, Logistic Regression, Model diagnostics, SAS |
Courses | Machine Learning by Stanford University on Coursera Machine Learning Foundations: A Case Study Approach by University of Washington on Coursera Machine Learning: Regression by University of Washington on Coursera Machine Learning: Clustering & Retrieval by University of Washington on Coursera Machine Learning: Classification by University of Washington on Coursera Introduction to Big Data by University of California, San Diego on Coursera Hadoop Platform and Application Framework by University of California, San Diego on Coursera An Introduction to Interactive Programming in Python by Rice University on Coursera Principles of Computing by Rice University on Coursera R Programming by Johns Hopkins University on Coursera Getting and Cleaning Data by Johns Hopkins University on Coursera Exploratory Data Analysis by Johns Hopkins University on Coursera Statistical Inference by Johns Hopkins University on Coursera Regression Models by Johns Hopkins University on Coursera Reproducible Research by Johns Hopkins University on Coursera SAS® Programming 1: Essentials SAS® Statistics 1: Introduction to ANOVA, Regression and Logistic Regression SAS® Enterprise Guide 1: Querying and Reporting SAS® Predictive Modelling Using Logistic Regression Coursera Computational Methods for Data Analysis |