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Резюме от 28 февраля 2018

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Этот соискатель решил скрыть свои личные данные и контакты. Вы можете связаться с ним со страницы https://www.work.ua/resumes/4696189

Junior Data scientist (machine learning), 12 000 грн

Занятость:
Неполная занятость.
Город проживания:
Киев
Готов работать:
Киев, Удаленно

Контактная информация

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Образование

Polish Japanese Academy of Information Technology

Computer science, warsaw
Неоконченное высшее, с 2015 по 2018 (3 года 1 месяц)

Дополнительная информация

Cover list:
Good morning/afternoon
Im a late year student of Polish-Japanese Academy of Information Technology. Im able to work on full time job.Here I would like to describe the experience which I have obtained during the studying.

SQL, SQL/PL, MS SQL experience:
During database course I have learned the principles of SQL, its rules and normalization. On practice I experienced (DDL) - creating of relation database, triggers, functions, indexes, procedures; updating, modifying tables and (DML) - communication with data base server, writing different types of queries and inserting, updating, deleting records.
In another course of database I have experienced MS SQL in correlation with .NET where I learned how to create web server with database and web page (first code, f), using MVC pattern.

Java experience:
For the past two years I have got Java core knowledge in addition I have cover communication between Java and SQL server using JDBC.
On a practice : Server - Client applications using sockets(TCP,UDP), Java Web-Service, Web-servlets, REST, JSON. Chat application, NIO

Python experience:
Ive started using Python for machine learning course in university.I learned: NumPy Pandas, Beautiful soup libraries and Matplotlibl for visualization data.
On practice: I implemented all universities projects from machine learning course which will be described below. In addition I have certain experience with JSON while creating chat bot for booking system where I used Beautiful soup for web scraping.

Machine learning
After course of Machine learning I learned different technics and methods of statistical leaning such as K nearest neighbor, Naive Bayes, Neural Network - as supervised learning(for language classification) and K - means clustering -as unsupervised learning. All this approaches I experienced on practice, using different data sets. Form data mining I implemented apriori algorithm .Moreover, I implemented Multi Layer Neural Network with backpropagation learning(in Python) for classification.

Statistic experience
From course of statistic. I learned the basic concepts of statistic: variance and standard deviation, different types distributions.I have strong understanding of random variables (discrete and continuous), probability distribution function, covariance , linear regression, logistic regression.
Due to curiosity, I have implemented both linear and logistic regression using just Numpy and made test cases using unit tests and scikit libraries.

Algorithms and Data structure
During this course I have learned all commonly used data structures (graph theory) and Time complexity of their methods. The most used algorithms of sorting and searching with their time complexity also were covered. I leann how to apply different data structures for efficient solution of different algorithmic tasks.

Best Regards, Volodymyr Kepsha.

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