In this project, I analyze a dataset and then communicate my findings about it using Python libraries NumPy, pandas, and Matplotlib to make my analysis easier.
After completing the project, I learnt:
- All the steps involved in a typical data analysis process
- Have familiarity in posing questions that can be answered with a given dataset and then answering those questions
- How to investigate problems in a dataset and wrangle the data into a format I can use for analysis
- How to communicating the results of my analysis and why it is necessary to initiate such communication with my Team
- How to use vectorized operations in NumPy and pandas to speed up my data analysis code
- How to efficiently work with pandas' Series and DataFrame objects, which helped me access data more conveniently
- How to use Matplotlib and Seaborn to produce plots showing my findings
- Step One - Choose a Data Set
- Step Two - Get Organized
- Step Three - Analyze Data
- Step Four - Share Findings
This project is part of the series executed in my Udacity Data Analyst program