Automating an ETL Pipeline w/ Python and MySQL

I find myself often working with data that is updated on a regular basis. Rather than manually run through the etl process every time I wish to update my locally stored data, I thought it would be beneficial to work out a system to update the data through an automated script. I use python and MySQL to automate this etl process using the city of Chicago's crime data.

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2019 TUN Data Challenge

Brief overview of the TUN Data Challenge I conducted with a team as part of SNHU experiential learning course. We work with client, marketing, and interaction data from non-profit, Hire Heroes USA to answer business problems specified by the organization. Our process involved: Defining goals, cleaning data, exploratory data analysis, statistical analysis, data visualization, and communicating results. Final results from the contest are still pending, expected in late July.

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Forecasting Economic Time-Series Variables in Excel

I grabbed some economic data from FRED and attempt to forecast future values for each variable using Excel. Econ variables of focus for this project include GDP, CPI, and the unemployment rate for the U.S. This was originally done for my Macro-econometrics class and provided me some nice time with Excel which I admittedly use too little.

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Nigerian Airline Frequency Analysis

A summary report of work I did for a Data Analytics II course at SNHU. Fictional company, Austin Air, is looking to enter the international airline industry with a goal of capturing a significant market share of business focused around Nigeria. Analysis will provide a look at the current industry and suggest strategies towards implementing a pilot program for the company to introduce to the market and achieve their goal.

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Return on Marketing Investment

Another project completed during Udemy's Tableau A-Z course. This was more focused on exploration and analysis of the data provided. Cluster analysis in particular was explored and evaluated throughout.

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Injured Worker Causes

Quick and dirty look at the causes of workplace injuries. Data obtained from and, while not a very comprehensive list (sample size is only 537), I'm using this as more of an experiment with Jupyter notebook and pelican.