Link to finalized projects that were reviewed and approved by Udacity in order to meet the requirements for obtaining the Data Analyst Nanodegree:
In this project, you will make use of Python to explore data related to bikeshare systems for three major bikeshare systems in the United States. You will perform data wrangling to unify the format of data from the three systems and write code to compute descriptive statistics. You will also make use of a package that is not part of the standard Python library to help you visualize the data.
Project Overview In this project, I analyzed a dataset and then communicated my findings about it. I utilized Python libraries NumPy, pandas, and Matplotlib to make your analysis easier. Dataset contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit history, and the latest payment information.
For this project, I worked to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. Your goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
In this project, you will investigate a classic phenomenon from experimental psychology called the Stroop Effect. You will learn a little bit about the experiment, create a hypothesis regarding the outcome of the task, then go through the task yourself. You will then look at some data collected from others who have performed the same task and will compute some statistics describing the results. Finally, you will interpret your results in terms of your hypotheses.
Your tasks in this project are as follows: Data wrangling, which consists of: Gathering data Assessing data Cleaning data Storing, analyzing, and visualizing your wrangled data Reporting on 1) your data wrangling efforts and 2) your data analyses and visualizations
The dataset that you will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage.
For the final project, you will create an explanatory data visualization from a data set that communicates a clear finding or that highlights relationships or patterns in a data set. Your work should be a reflection of the theory and practice of data visualization, and your final deliverable will be a write up along with a Tableau Public workbook.
We will provide some options of data sets to explore; however, you may choose to explore an entirely different data set. You should be aware that finding your own data set and cleaning it using Python, R, or some other language can take considerable time and effort. This can add as much as a day, a week, or even months to your project so embark on the adventure to find and clean a data set if you are truly prepared with data wrangling skills.
You have three options for this project. You should pick an option based on your prior experience with data munging and exploratory data analysis. The option you choose will not affect the evaluation of the project.