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Projects

Drawing on the Our World in Data organization's Covid19 dataset, this project provides an overview of the scope of the Covid19 in Haiti. The results shed light on both the total cases, total deaths, death percentage, infection rate and the percentage of vaccinated people.

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Nothing helps to boost a decision-making process like clean data. Inaccurate data can be dangerous, as it leads to false conclusions, making even the most sophisticated algorithm fail. 

That's why data cleaning is so important because it removes incorrect, wrongly formatted, and incomplete data within a dataset. In this project, I will be practicing some of the most common data cleaning techniques in SQL. 

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Using data from my previous project Data Exploration in SQL, this dashboard helps us get a clear idea of what the information found means, and provides an accessible way to see and understand the patterns in the data by using visual elements such as charts, graphs, and maps.

View dashboard in Tableau Public

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In this project, I extract data from the job search and career opportunities website Jobpaw and save the collected data in a csv file. The data present the company, job title, field and deadline of all job offers published on the website from September 2010 to January 2022.

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Regression analysis and correlation tests are very helpful nowadays, as they help us understand the relationships between variables in this large amount of available data. Using three decades of movies data, in this project, I try to understand which variables does indeed have an impact on the Gross revenue of the movies.

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Using publicly available Microsoft financial statement data, in this project I created an interactive dashboard, presenting both the Balance Sheet and the Income Statement of Microsoft from 2021 to 2016. 

View dashboard in Power BI.

 

I also created a report comparing the budget, forecast and actual expenses of the IT department of a global company. 

View report in Power BI.

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This project is a simple binary choice model used to make predictions about customer buying behavior based on their historical transaction data.

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