Data Analyst Portfolio Project Walkthrough (Excel & SQL)

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 In this video, we take a look at a Data Analyst portfolio project that utilizes both Excel and SQL. The goal of the project is to run some initial analysis on data for last pitches for the Tampa Bay Rays. Everything is coded within MSSQL and inside SQL Server Management Studio. Github: https://github.com/RyanNolanData/Portfolio-Projects Baseball Savant Data: https://baseballsavant.mlb.com/statcast_search Baseball-Reference Data: https://www.baseball-reference.com/teams/TBR/2022.shtml 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ Dive deep into MLB pitching data with this comprehensive SQL analytics project focused on the Tampa Bay Rays 2022 season. In this video, I analyze real pitch-by-pitch data from Baseball Savant and Baseball Reference to uncover insights about one of baseball's best pitching staffs. We explore everything from pitch sequencing and strike zones to home run tendencies and pitcher efficiency. This project demonstrates advanced SQL techniques including CTEs, window functions, complex joins, subqueries, case statements, and partition by clauses. Whether you're building your data analytics portfolio or learning SQL, this real-world analysis covers 20+ queries ranging from beginner to advanced difficulty. Perfect for aspiring data analysts looking to showcase baseball analytics skills or anyone interested in how data reveals the strategic side of America's favorite pastime. **GitHub Repository:** All code, datasets, and questions available in the description below. **Key Analysis Areas:** - Pitch sequencing patterns and efficiency metrics - Strike zone analysis and pitch location tendencies - Home run analysis by count, zone, and pitch type - Shane McClanahan deep dive (top Rays pitcher) - Relief vs Starting pitcher comparisons - Launch angle and exit velocity correlations #SQL #DataAnalytics #BaseballAnalytics #PortfolioProject #TampaBayRays #MLBData #DataScience #sqltutorial TIMESTAMPS 00:00 Introduction & Project Overview 01:50 Downloading Data from Baseball Savant 05:59 Understanding Baseball Statistics & Data Fields 11:40 Cleaning & Formatting Excel Data 20:30 Data Validation & Column Selection 28:50 Downloading Baseball Reference Data 35:40 Mapping Pitcher IDs Between Tables 43:20 Importing Data into SQL Server 51:00 Question 1A: Average Pitches Per At-Bat 55:20 Question 1B-D: Home/Away & Lefty/Righty Analysis 01:05:40 Question 1E: Top Three Most Common Pitches (Advanced Query) 01:14:20 Question 1F: Average Pitches by Innings Pitched (Join Query) 01:20:40 Question 2A-B: Last Pitch Analysis & Fastball vs Off-Speed 01:30:00 Question 2D: Relief vs Starting Pitcher Analysis (Subqueries) 01:38:20 Question 3A-C: Home Run Analysis by Pitch & Zone 01:49:40 Question 4A: Shane McClanahan Stats with Complex Join 01:56:40 Question 4B-D: Position Analysis & Final Queries 01:58:40 Closing Remarks OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.