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brief version (pages 1-2) and full version (pages 3-13) Dmitry Mugtasimov Senior Python Developer / Team Leader email@example.com https://www.upwork.com/fl/dmugtasimov Skype:
Table of Contents Dive Into Python...............................................................................................................................................................1 Chapter 1.
Christina H Ziegler Wilmette, IL, 60091| firstname.lastname@example.org| 847-525-4956 https://github.com/ChriZiegler PYTHON DEVELOPER Skilled and solutions-oriented professional with relevant hands-on experience designing and developing webbased applications including ecommerce websites.
Python PARA TODOS Raúl González Duque Python PARA TODOS Raúl González Duque Python para todos por Raúl González Duque Este libro se distribuye bajo una licencia Creative Commons Reconocimiento 2.5 España.
It displays all major cricket related statistics from scraped data set in Python.
Mike Johnson Jr 4859 Cedar Springs RD #329, Dallas, TX Michael.email@example.com http://mikejohnsonjr.com 214.417.3363 WORK EXPERIENCE Software Consultant ZProcessApps – Santa Cruz, CA – August 2016 to present (part time) Python/Django web development for subclients such as the FoodFeedback App for iPhone and Android (backend web API) and vipautoappraisal.com.
Experience June 2016 – Innovator / Python Developer, Digital Impact Square, Nashik, Maharashtra.
Cyrus Maden cyrusmaden.com • firstname.lastname@example.org • ( 707) 8130036 Education Brown University ● Providence, RI; expected graduation: May 2018 ● A.B. Applied Mathematics, A.B. Urban Studies; GPA: 4.0/4.0 Professional Analytics Experience The Providence Plan , Data Analyst Intern Providence, RI — fall 2016 ● Consult on 4 statistical computing and analysis projects for the Department of Labor and Training, National Institute of Justice, and the RI Office of Postsecondary Education ● Manage and perform statistical analysis for 5 statewide workforce and education datasets of over 20 million records in Python and SPSS ● Perform data management and visualization to produce information services from statewide datasets, to support 2 policy reports on RI food policy and RI employment outcomes Rhode Island Division of Planning , GIS Intern Providence, RI — summer 2016 ● Wrote a technical report on multivariate statistical modeling with GIS to track land cover trends for 5year updates of the statewide land use and longrange transportation plans ● Selected to present findings from the technical report to all 40 members of the RI Statewide Planning Council and publish research to the general public ● Scripted an ArcGIS geoprocessing tool in ArcPy to automatically map the future land use plans of RI's 39 municipalities to keep track of municipal land use updates and save over 100 hours of work per update Technical Communications Experience Better World by Design , C ommunications Coordinator Providence, RI — 2015current ● Lead the communications team on the planning committee for the ninth Better World by Design conference, a studentlead design conference with over 700 attendees from 4 countries ● Manage working relationships with 5 professional design and engineer organizations, and use communication and analytics tools to increase our network in the professional community by 20% ● Develop analytics reports with Hootsuite and Python to increase the reach our content by 25% and increase our online community by 400 members Academic Research Experience Brown University Urban Studies Program , R esearch Assistant Providence, RI — 2015current ● Design research methods to model gentrification trends in Los Angeles with statistical computing in ArcGIS and Python ● Develop an ArcGIS geoprocessing tool to synthesize 2 or more polygon features into Censusdesignated boundaries, and scripted an open source version of the tool in Python ● Build 2 regression models to analyze crime patterns over 30 years in Providence and Los Angeles using REST APIs for data management and Python for processing and analysis Skills and Interests ● ● ● I love: public speaking, technical writing, Python, ArcGIS, APIs, Tableau, SPSS, and Excel I’m a passionate beginner with: web maps, Unix/Linux, MATLAB, and juggling I’m also an: aspiring soccer player, blogger, and amateur competitive eater
Selenium - O que você deveria saber - Parte 1 // Tags selenium python selenium-serie Esse é o primeiro post da série sobre Selenium, pretendo cobrir desde o básico até algumas coisas mais legais :) Introdução Selenium é um ótimo framework para realizar diversos tipos de tarefas com o browser.
+79166391685 languages Data mining, Machine learning, Applied statistics, Data visualisation, Python, C++, Functional programming, Data analysis and statistics based services skills C/C++(C++11), Linux, Git, Python.
Bangalore 2015 TEC HN I C A L SK I L L S Proficient in C, Java, Python and C++98 Basic knowledge of HTML, CSS, Android Other :
As a result, professionals, hobbyists and students introduced to the field can be significantly overwhelmed by the constantly changing relationships among those technologies. Introducing a simpler way of viewing and clustering technologies within the field of computer science would help people get a clearer picture of the application and trends of the technologies people are using. As an example, someone being introduced to Python for the first time might understand the syntax and basic applications, but won’t be able to recognize how Python interacts and fits in the grand scheme of technologies. For the characterization of such technologies we used Stack Overflow as a source of data directly related to the field trends. Stack Overflow is a popular internet platform for people to ask and answer technologically relevant questions. We used their API to gather the tags of each post and construct a similarity network based on the frequency two tags are seen together. We chose to visualise our results by constructing a node map using the network analysis library networkX and the visualization package Gephi . The visualization contains each note as the tag and all the vectornodes as the related technologies. Methods and Techniques We constructed a network out of the 1000 most 1 popular tags and examined the characteristics of our network. Each network node reflects a technology tag and each connection represents a directional dependency to other tags. In order to measure the dependency we computed the conditional probability of each tag appearing with another. This metric implies that each tag combination has 2 relationships that it can be characterized with. One is the relationship of tag1 to tag2 and, conversely, the relationship of tag2 to tag1. We chose dependency over the jaccard distance of two tags due to hub nodes being connected to so many other nodes, and, as a result, the strength of the connection becomes diluted. As an example, python’s connection to pandas has an index of 0.08 (8% of all python posts are about pandas) but pandas’ connection to python is 0.9, meaning that 90% of all pandas posts also include the “python” tag. In order to consider a connection between two tags as valid, we set a threshold of d(1,2) > 5%.
2013 – May 2017 COURSEWORK Intro to Machine Learning, Neural Computation, Probability and Random Processes, Optimization Models, Signals and Systems, Computational Photography, Operating Systems, Relativity and Quantum Physics WORK EXPERIENCE Microsoft Corporation Summer 2016 Software Engineer Intern Designed and implemented new framework for Microsoft Azure that focuses on engineering principles and reliability in Python and C++ Developed skills in computer networking and operating systems Was among a select few interns invited to pitch project to the CTO and CVP of Microsoft Azure Undergraduate Computer Vision Researcher Jan.