John Noshita

Intro

I have extensive professional experience in analytics of credit and finance. I am leveraging this experience with a skillset for data analytics that includes: certified Excel and PowerPoint, along with Python coupled with BeautifulSoup, Seaborn and Pandas libraries, Tableau and SQL specifically PostgreSQL.
Check out my work.

Work

HR workforce planning model: This comprises of an Excel workbook that forecasts several workforce and compensation strategies with a goal to increase revenue along with a PowerPoint presentation of the findings for the interaction of such strategies.
HR Compensation Tool

Rental fleet purchase planning: An Excel workbook of rental fleet revenue data for a fictional rental vehicle business that would be the ninth largest in the US based on fleet size. Also includes a PowerPoint presentation on strategies planned to minimize costs and increase revenue.
Capstone - Project #1

Data analysis of Kaggle dataset on housing prices: An Excel workbook that includes A/B testing and regression modeling used to identify variables that have greatest impact on home sale prices. A PowerPoint presentation on the findings is included which makes recommendations on portfolio purchasing for mortgage-backed securities based on a review of real estate market and general economic futures indicators.
Capstone - Project #2

Making an API request through Python: Here's a Jupyter notebook created with Google Colab that fetches data via the Harvard Art Museum website API.
Drill: Python API request

SQL data retrieval in Python: Here's another Jupyter notebook created with Google Colab that retrieves data using SQL queries.
Drill: SQL data retrieval in Python

Web scraping through Python: Here's another Juputer notebook created with Google Colab that scrapes data on news stories from the space.com website using the requests and BeautifulSoup libraries.
Drill: Web scraping in Python

PGA Tour Historical Data Analysis: This is a PDF printout of the Jupyter notebook created with Google Colab with analysis of historical PGA Tour data from the 2010 through 2018 seasons. This analysis focused on finding statistics that correlated with lower scoring averages for professional PGA Tour players. Those statistics identified were entered into regression modeling and tracked for the shift in importance over the 9 seasons tracked. Then, the same statistics selected as independent variables in the 2018 regressional model were reviewed to determine if any could guide me as an amateur golfer on how I can adapt my practice habits to address the most important of statistics identified with the goal of lowering my own scoring average (handicap). The reason for a PDF of the Python notebook is because the file includes 300 separate code and text markup cells that can take a very long time for processing depending on your machine. Also, it requires a .csv file that is over 240 MBs which must be stored on your local machine (and the notebook would need to be edited to point to the local drive where the datafile is stored). As such, the PDF is included here which shows the execution of the entire notebook. Links are included in the PDF file to other files used in the analysis as well as to the Kaggle webpage where the .csv dataset file is located.
Final Capstone - Project #3

About

I believe in the power of the analysis of data done with integrity. It can be transformative when performed without agenda. I am a relatively new devotee to the discipline of data analytics, but I am eager to embark on a continued, lifelong journey to keep learning and growing within the field. I have a medium term goal of attaining the skills needed to become a data scientist.

Contact and Additional Info

Email: dextraur@yahoo.com Click here to send me an email

• LinkedIn
• Tableau

Elements

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