Description

Je suis Data Analyst. J'ai 2 ans d'expériences entre freelance, stage à l'étranger et projets personnels… J'ai eu la chance de travailler avec plusieurs start up pour l'analyse de donnée specialement la prédiction et la recommandation dans le domaine du Marketing et Ecommerce, j'ai aussi une certification Google pour le Data Analyst, Data Visualization.

Grace au différentes experiences qui m'ont permit de me developper et de pouvoir travailler avec différents outils pour la visualisation selon le besoin(Power Bi, Tableau, Excel...)

Et aussi developper des Modeles du Machine Learning qui s'adapte selon le besoin.

Mes compétences:

o Python - Matplotlib - Seaborn

o SQL

o Power Bi - Tableau

o Méthode Agile

o Scrum Master(ref de la certif: 66S9KULLVQ3V)

o Python( pour l'intelligence artificiel certifie par Huawei ref: 010102001287808826691409)

Languages

French
Native or bilingual
English
Fluent
Spanish
Conversational
German
Basic

Workplace preferences

Can work on-site

Toulouse (up to 50km), Paris (up to 15km), Bordeaux (up to 15km), Lyon (up to 10km)

Spotify Spain Sl - Spain
Data analysis using Pyton on music Industry
ENTERTAINMENT AND LEISURE
January 2022 - January 2022
This project is made to perform exploratory data analysis using Pyton on music
related datasets. Then using a tool for visualization Power Bi.
Spotify is the world's largest audio streaming platform.
The step we will follow in this project are:

Import our data
Check and Analyze the Data
Figure out the correlation
Visualize the data with Python librairies
Use PowerBi for more visualization

Tools Used:
Python, Pandas, Seaborn, Matplotlib
PowerBi
Jupyter, Gitlab
Python Pandas Seaborn Microsoft Powerpoint Jupyter Git
FunguyMushrooms
Dévelopeur Web
ARTS AND CRAFTS
December 2021 - January 2022
Dubai Design District, United Arab Emirates
Amazon
Analyze the amazon Reviews
E-COMMERCE
January 2021 - January 2022 (11 months)
États-Unis, France
We will walk through a real-world python machine learning project using the sci-kit learn package.
In this project, we will create a model that automatically classifies text as having a positive or negative or neutral emotion. We accomplish this by using Amazon reviews as training data.
We will apply it for Books reviews, you can use it for any other article's review

Tools and librairies used

Python
Pandas
Json
matplotlib
random
sklearn (model_selection, feature_extraction.text, svm, DecisionTreeClassifier, GaussianNB, LogisticRegression)
sklearn.metrics (f1_score)
Pandas Python Analyse de données sklearn Metrics F1scores Data science Machine learning