Data analysis

Below are outcomes that I produced throughout the data analysis process as part of the curriculum for my Programming for Data Science with Python nanodegree.

Exploration: Cleaning and summarizing

As part of my nanodegree program through Udacity, I had the opportunity to explore and clean a provided data set in preparation for final analysis. Scroll through the Jupyter notebook below that I used to document this process.

Skills involved: Python and data analysis packages (Pandas, NumPy, Seaborn, and Matplotlib), Jupyter Notebooks, data exploration, and cleansing.

Analysis: Visualization and findings

To present findings from the cleansed dataset described above, I prepared slides describing exploratory questions for analysis and accompanying visuals of relevant data. Browse through the Jupyter slideshow below to explore my findings from this process. Click the arrows in the bottom-right to advance slides. Where applicable, click the down-arrow to view visuals referenced in the text.

Skills involved: Python and data analysis packages (Pandas, NumPy, Seaborn, and Matplotlib), Jupyter Notebooks, data visualization.