Goal: Use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions
Learn to:
- Wrangle, extract, transform, and load data from various databases, formats, and data sources
- Use exploratory data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets
- Classify unlabeled data or predict into the future with applied statistics and machine learning algorithms
- Communicate data analysis and findings through effective data visualizations
Projects for my Udacity Data Analyst Nanodegree
Get started on your Nanodegree and learn how to make the most of Udacity's resources!
You’ll also do your first project analyzing bike rental data. It’s a great project to tackle during the first week in your Nanodegree to see if the program is a good fit for you!
Learn to use descriptive statistics to describe properties of datasets and learn about how samples and populations are related.
Choose one of Udacity's curated datasets and investigate it using NumPy and Pandas. Go through the entire data analysis process, starting by posing a question and finishing by sharing your findings.
Choose a region of the world from www.openstreetmap.org and then use data wrangling techniques to audit and clean the data. You'll then use a database to query the cleaned data.
Use R and apply exploratory data analysis techniques to explore a selected data set for distributions, outliers, and relationships.
Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology.
Play detective and put your machine learning skills to use by building an algorithm to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
Understand the importance of data visualization. Know how different data types are encoded in visualizations. Select the most effective chart or graph based on the data being displayed.
