Figura professionale: Python Developer, Electronics & Communication Engineering
Nome Cognome | : S. R. | Età | : 31 |
---|---|---|---|
Cellulare/Telefono | : Riservato! | : Riservato! | |
CV Allegato | : Riservato! | Categoria CV | : Developer / Web dev. / Mobile dev. |
Sede preferita | : Milano |
Accesso Full al database con 29.999 CV a partire da € 5,00 ABBONATI SUBITO!
Sommario
Esperienze
WORK EXPERIENCE
Freelance work
Freelance work on Excel VBA and Pythonprogramming language
Achievements/Tasks
creation of macros and questionnaire tools related to company projects
Data visualization and Data analysis for Automobile company's
11/2018 – 03/2019 Internship
STARA GLASS S.P.A., Genova (Italy)
Creation of a Database and Development of a Model to
Identify a Set of Metadata to Define a Dynamic and
Questionable Tool. Thermal Analysis of Thermal Bridges Over Crown in Furnace
Worked along with multicultural teammates
With limited supervision completed the task before deadline and got appreciation from higher management
EDUCATION
2018 – 2019
MIPET-Mater in Industrial Plants
Engineering and Technologies
Universita degli Studi di Genoa
this course is related to industrial automation
Virtual and Augmented Reality
industry 4.0
2014 Bachelors in Electronics & Communication Engineering
Jawaharlal Nehru Technological University, Hyderabad (India)
SKILLS
Python (Programming Language) Data science
Data visualization Machine learning Data Analysis
Microsoft Office
Excel VBA (Visual Basic for Applications) C Tableau SQL
PERSONAL PROJECTS
Data Visualization with Haberman Dataset
Diagnose the Cancer using Haberman’s Data set with the help of data
analysis techniques and by using various Python libraries.
Apply t SNE on Donors Choose dataset
Exploratory data analysis of the features in dataset
Build the data matrix using these features plot t-SNE plots with each of these feature sets Concatenate all the features and Apply t-SNE on the final data matrix
Apply k NN on Donors Choose dataset
preprocessing the features of dataset(categorical, numerical features)
Hyper paramter tuning to find best K
Appling KNN on different kind of featurization
Apply Naive Bayes on Donors Choose dataset
Applying Multinomial NaiveBayes on these feature sets The hyper paramter tuning(find best Alpha)
Applying NB() on different kind of featurization
LANGUAGES
English
Full Professional Proficiency
Italian
Limited Working Proficiency
Telugu
Native or Bilingual Proficiency
Hindi
Full Professional Proficiency
INTERESTS
working on python working on data analysis
Excel VBA Exploring Machine learning
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