This dashboard contains stat indicators for NBA players between the 2010 and 2020 seasons. The data for building this dashboard were obtained through Web Scraping, using Python, directly from the official NBA website.
When selecting the player you will be able to analyze the averages of the total points, minutes played, points per minutes, rebounds, assists, free kicks, basket attempts, converted baskets.
The graphs show the average points per game vs average minutes played per game, average points per minutes per season, historical series of average baskets made, baskets of 2 points and baskets of 3 points.
On the second page, you can see the evolution of the leaders in points per minute played per season
You can see my medium post commenting on how to get the data through Web Scraping: Link
This dashboard contains indicators of deaths and new cases of Covid in Brazil, from the beginning of the pandemic until April 2021.
You can select whether the graph on screen 1 will display the total cases or total deaths indicator. On screen 2, you can observe the ranking of deaths or total cases by state and also observe the concentration on the map graph.
This Dashboard contains indicators about the profit of a certain company operating throughout the American territory. It is possible to observe profit by state, region, segment (Customer, Home Office or Corporate), quantity of products vs type of shipment requested, Profit vs Quantity of products sold per month, Ranking of products that generate the most profit and ranking of products that cause the most financial loss. When hovering the mouse over the tree graph on screen 1, the ranking of states according to Profit will be displayed for each region, showing which one presents profit or loss.
This panel contains indicators of the number of admissions, dismissals, average length of service, average salary among employees who left, number of admissions by source of recruitment and turnover.
Screen 2 shows the historical series of admissions to the company
This Dashboard presents indicators of total demands, average time for the demand phase, Lead time, Agging and on the last screen, a mathematical model was generated that estimates the chance of the project being late based on the delay or delivery on time for the phases
The mathematical model was developed using logistic regression