Scenario
You are a data analyst for a basketball team. You have found a large set of historical data, and are working to analyze and find patterns in the data set. The coach of the team and your management have requested that you use descriptive statistics and data visualization techniques to study distributions of key variables associated with the performance of different teams. Data-driven analytics will help the management make decisions to further improve your teams performance. You will use the Python programming language to perform your statistical analysis. You will also need to present a report of your findings to the teams management. Since the managers are not data analysts, you will need to interpret your findings and describe their practical implications. The managers will use your report to find areas where the team can improve its performance.
Reference
FiveThirtyEight. (April 26, 2019). FiveThirtyEight NBA Elo dataset. Kaggle. Retrieved from https://www.kaggle.com/fivethirtyeight/fivethirtyeight-nba-elo-dataset/
Directions
For this project, you will submit the Python script you used to make your calculations and a summary report explaining your findings.

Python Script: To complete the tasks listed below, open the Project One Jupyter Notebook link in the Assignment Information module. Your project contains the NBA data set and a Jupyter Notebook with your Python scripts. In the notebook, you will find step-by-step instructions and code blocks that will help you complete the following tasks:

Choose and create a data visualization.
Calculate descriptive statistics including mean, median, min, max, variance, and standard deviation.
Construct confidence intervals for a population proportion and a population mean.

Summary Report: Once you have completed all the steps in your Python script, you will create a summary report to present your findings. Use the  provided template to create your report. You must complete each of the following sections:

Introduction: Set the context for your scenario and the analyses you will be performing.
Data Visualization: Identify and interpret your chosen data visualization.
Descriptive Statistics: Identify and interpret measures of central tendency and variability.
Confidence Intervals: Identify and interpret the lower and upper limits of  confidence intervals.

Conclusion: Summarize your findings and explain their practical implications