Mario Hoxha

MICROSOFT AZURE CLOUD TECHNOLOGY - Azure Architecture - Azure Data Factory - Databricks - Azure SQL DB ANALYTICS SERVICES AND DATA WAREHOUSE SOLUTIONS - Build analysis and visualizations models using Power BI - Design and Implementation of Data Warehouse Solutions and Architectures OTHER TECHNOLOGIES AND PROGRAMMING LANGUAGES - Use Python to build prediction models - SQL Server Management 2019 -Past Experience of C & C++

Making value out of data from Bosch plants. Proactively fetching info from various source using SQL Management Studio 14 and 17. Making use of Visual Basic, SQL and Python to build tools which automate certain processes within the company, unifying the data and raising data quality. Analyzing and visualizing the data using Power BI. Extracting data inside Power BI and transforming it to knowledgeable meaningful information, easy to be interpreted. Visualize and interpret the results and the statistics.

• Data analysis in Python using Pandas library • Data mining using Python • Running machine learning algorithms • Building a predictive Loan Model

•use C to code the algorithm which would produce the data •the algorithm takes 4 bits as an input and it gives the complexity of graph formulas as an output •use Python to make 2D&3D visualizations out of data •continuing using Python to allow user to interact with the visualization •on click event at any given point of the visualization it will display how the point was obtained, what where its predecessors •used python to add other features •try to gain some insight from the data that we visualized

• programmed a Intel Edison SoC using C & C++ • implementing a ping-pong game which could be played in network by 4 players on 4 different Edison’s • adding graphical interface and motion sensors to the game • establishing the communication and its protocol among the Edison’s

•studying information diffusion and information cascades of Twitter •collecting Twitter Streaming API data from the Olympic Games 2012 •using python to code metrics like in&out degree, shortest paths, closeness centrality, betweens centrality •continuing using python to turn our metric into features like standard deviation, skewness and kurtosis •perform unsupervised machine learning algorithms •accomplish feature selection and feature extraction •interpret the data •evaluate the results