NHL Gameplay Analysis:

Utilized Python to process and analyze NHL gameplay data, visualizing player strategies on a rink representation, predicting puck trajectories, and assessing player influence through spatial coverage. Skills honed in data visualization, geometric algorithms, and sports analytics.

Data Analysis and Visualization with Power BI:

Created six dynamic Power BI reports by seamlessly connecting to SQL databases and CSV data, enhancing data integration and visualization. Proficient in data modeling with star schema, ensuring data accuracy. Utilized DAX functions for calculated measures, enabling data-driven decision-making.

ESPN Fantasy Football Data Analysis:

Leveraged Python for data extraction and analysis of ESPN league trends. Utilized visualizations with Seaborn and Matplotlib, deriving actionable insights. Deepened understanding of performance metrics and effective team strategies.

NHL Draft Performance Analysis:

Analyzed the NHL to determine the success factors behind the Tampa Bay Lightning's remarkable performance. Employed Python for ETL, scraping player data from 32 teams using Beautiful Soup and then integrating this into a MySQL database. Through data cleaning and aggregations in SQL, I uncovered player drafting strategies. Enhanced capabilities in web scraping, data ingestion, and SQL operations.

StarCraft Player Performance Analytics:

Under a tight 5-hour deadline, I conducted an in-depth analysis on a StarCraft dataset to discern the factors contributing to a player's rank. Using Python, the analysis entailed data cleaning, EDA, and applying multiple machine learning models, including regression techniques and neural networks. Key insights showed that features like ActionLatency and APM significantly affect player ranking. The project enhanced my understanding of data intricacies and the importance of meticulous feature engineering in predictive analytics.

Additional Projects and Profiles

Scott Christensen's Tableau Page

Tableau Projects displaying interactive dashboards that showcase data visualation skills.

Interactive Sales & Data Analyst Survey Dashboard:

Leveraging Power BI, this dynamic dashboard visualizes intricate sales patterns and captures insights from a survey of data analyst professionals. Users can delve deep into sales metrics and simultaneously gauge the preferences and trends among data analysts, all through a user-friendly interface.

SQL Operations and Address Standardization

Utilized SQL to transform and optimize a Nashville Housing dataset, including tasks like date standardization, address segmentation, and deduplication. This experience underscored my proficiency in leveraging SQL for meticulous database cleaning and management.

Predictive Analysis of Song Popularity using Spotify API

Leveraged Spotify's API and Python to predict song popularity based on audio features. Applied data scraping, cleaning, and modeling techniques, using tools like Spotipy and AutoSklearn. Uncovered key audio characteristics influential in song success, enhancing insights for music industry professionals.

Time Series Analysis and Forecasting of Adjusted Close Price

Analyzed time series data of a financial institution's adjusted close price using exponential smoothing techniques to forecast potential business trends. Utilized Python for data processing, manipulation, and model building. Enhanced forecasting skills.

Cyclistic Rider Behavior Analysis

Using Python, I analyzed Cyclistic's historical trip data to discern differences between annual members and casual riders. Through data cleaning, feature engineering, and visualizations, I found casual riders favor longer rides with docked bikes. These insights can guide tailored marketing campaigns to convert casual riders into annual members.