A highly driven Data Analyst adept at leveraging a dynamic blend of analytical prowess, proficiency in business intelligence tools, technical writing, coding expertise and a wealth of practical experience to deliver valuable insights and data-driven solutions for organizations.
This full data analysis case study involved analyzing the data of a fictional biking company called Cyclistic to figure out how to convert their occasional users into full time subscribing members. The data contained over 4 million entries across a number of key metrics. The tools used for the analysis include R for data cleaning and analysis, Tableau for data visualization and Microsoft PowerPoint for a full presentation of findings.
In this project, I used data modeling, DAX, report design, and advanced data visualization in Microsoft Power BI to create a report to show a comprehensive overview of supplier quality and performance for a fictional company, highlighting key metrics such as defect quantity, downtime and their impact on overall operations.
Here, I combine two of my interests; football and SQL to analyze Barcelona's 2015/2016 league season; looking at their form, their goal scoring and other relevant metrics that can be used to paint a broad picture of their performance in that league season.
Here, I built a recommendation system using Python ML algorithms. The project also involved data cleaning, data transformation, data analysis and data visualization, all performed top to bottom with python.
For this project, I utilized Microsoft Excel, Microsoft PowerPoint and Microsoft Power BI to create an executive level KPI dashboard for a fictional import and export business called Northwind Traders to display company performance in key areas such as sales trends, product performance, key customers and shipping costs amongst others.
Technical article intended to serve as an introduction to Azure for prospective data professionals. Focused mainly on guiding a new user through setting up for the more elementar data applications of Azure like data storage and Azure SQL.
This project uses Python to analyze movie data from Kaggle, aiming to determine the key factors influencing a movie's gross earnings. I specifically explore whether the production company behind a movie plays a significant role in it's financial success, challenging common assumptions about factors like budget or lead stars.