The NBA Player Evolution Analysis, using Python, aims to analyze the historical trends of NBA players over the past 70 years. The analysis will focus on the median heights and wingspan lengths of players by position, as well as the variation in these physical attributes over time. Data for the analysis was collected from multiple sources, including a Kaggle dataset and Basketball Reference, to obtain year-by-year player statistics from 1950-2022. The analysis seeks to determine if there are trends in the height and wingspan of NBA players and if there has been an increase in uniformity or standardization among players.

In this dashboard using Tableau, we see a country-by-country and year-by-year dynamic display of various performance indicators that change with user selection of country and year. In addition, an overtime analysis of the given country, where the user selects which variable to see the country trend.

The analysis using R focuses on the relationship between GDP per capita, CO2 emissions per capita, and net barter terms trade index for various countries using data from the World Bank. The data is cleaned and transformed to be in a tidy format for analysis. Log10 transformation is applied to variables for better visualization, and centering and standardizing are done for improved data distribution. Finally, the data frames are merged and displayed for further analysis.

The analysis using R focuses on visualizing the education levels of different countries, specifically comparing Afghanistan and Aruba. The data is read in from two separate datasets, electricity.csv, and secondary_edu.csv, and cleaned using dplyr functions. The datasets are then merged using left_join. The resulting data is visualized using ggplot2, with lines and points representing education levels over time. The plot includes legends for country differentiation and is filtered to display relevant years of data.

In this SQL script, using Microsoft SQL Server The SQL query retrieves and manipulates data from multiple World Bank variables, including population, GDP, and electricity, to create a view called "WorldBankVariables". The query involves data cleaning, updating, joining, and using various SQL functions to calculate rankings, differences, and percentage changes. The final result is sorted by Country, Year, and variable values.

The analysis examines the 2022 Midterm results in Maricopa County using R, focusing on turnout, split-ticket voting, and the impact of Libertarian Party candidates on Republican candidacy results. Historically, Midterm turnout is lower and split-ticket voting has decreased. Research suggests Libertarian spoilers may have influenced past election outcomes. Data was collected from the Maricopa County Elections Department. The analysis provides a comprehensive overview of relevant literature and data collection methods for further analysis.

The analysis uses R and the gapminder dataset to explore changes in life expectancy over time. It calculates the mean and standard deviation of life expectancy for each continent year and plots them as lines and ribbons, and facets by continent. It also plots individual country-year values of life expectancy as points on a plot, filters out countries in the top or bottom 20% of observations, calculates variance between country-year values and continent-year means, and varies point opacity based on variance. Finally, it calculates the mean population for each continent year, creates a binary variable for population analysis, and colors points accordingly.

In the analysis using Python tools we will analyze customer patterns over various times of the year to create promotion tools and build a potential advertizing campaign.