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20181010 ILO ( TELECOM PARISTECH 2016 ) CONSULTANT BIG DATA DATA ENGINEER DATA ANALYTICS

Paris €800/900 / jour 2018-FINDING-Dossier-de-compétence-20181010-ILO-V2.pdf
Building Predictive ModelsBuilding Web AppData AnalysisData ExtractionData PreparingData scienceDATAVISUALISATIONHadoopIndexing & Analyzing dataintégrationJava JEEPublishing ModelsTestValidation
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SCOPE D INTERVENTION

  •  Data Extraction
  • Data Preparing
  •  Indexing & Analyzing data
  • Building Web App
  • Building Predictive Models
  • Publishing Models
  • Test, Integration et validation
  • Deployment

PROJECTS

 Since May 2017 – BIG DATA DEVELOPPER – CONFIDENTIAL – (RP)

Projects  :

SCIPION : Application to alert clients on Mobile network incidents (End to End dev)

SQL/UCO : Application for extracting, transforming and loading data (End to End dev)

Tools : LINUX, SHELL, Bash, HADOOP, SQL, HIVE, HBASE, PIG, OOZIE, SVN

 

2015 /2016 –  SOFTWARE ENGINEER – CONFIDENTIAL – (RP

Projects  :

SISMIP PROJECT : Web Application for medical service management in Guyana

➢ Developing Web Application JAVA JEE

➢ JSF, HTML, JBOSS, PostgreSQL, SVN

➢ Test, Integration & Validation

TIPI PROJECT : Restitution of Departmental Housing Management Data

➢ Developing Web Application Java JEE using Birt plugin

➢ TOMCAT, Servlet/JSP, HTML, PostgreSQL, SVN

➢ Test, Integration and validation

GRENAT PROJECT : Restitution of management data for nature reserves in France

➢ Developing Web Application Java JEE using Birt plugin

➢ Servlet/JSP, HTML, Struts2, PostgreSQL, SVN

➢ Test, Integration et validation

Tools : JAVA J2EE, BIRT, STRUTS, JSP, Linux, SVN, TOMCAT, JBOSS, HIBERNATE, LOG4J, POSTGRESQL, JSF

 

2014  –Apprentice Web&TV Analytics (Software Engineering)  – CONFIDENTIAL – (RP)

Main tasks  :

➢ Data Extraction (Teradata, Hadoop, Aster, MongoDB)

➢ Data Preparing (SQL, HiveQL, TeradataSQL)

➢ Indexing & Analyzing data (SolR, ElasticSearch)

➢ Building Web App (HTML5, CSS3, PHP, Javascript, Highcharts, D3JS, Kibana)

➢ Building Predictive Models (R ‘caret package’, Python ‘sklearn’, Scikit-learn). : Methods:(learning et test), Feature Selection, GridSearchCV, Algorithms (RF, GradientBoost, SVM, KPPV, Adaboost, PCA,..)

➢ Publishing Models (Rshiny, Rpubs, Git)

➢ Test and Deploy (Linux, AWS, Windows)

Tools : AWS, LINUX, SHELL, Bash, TERADATA, HADOOP, MONGODB, ASTER, SQL, SPLUNK, HIVE, ELASTICSEARCH, SOLR,JAVASCRIPT, HTML, CSS, MACHINE LEARNING, R (caret package), RShiny, HIGHCHARTS, D3JS

Éducation

2016 at TELECOM PARISTECH