Excel - Serie A

Immerse yourself in the exhilarating world of Serie A  analytics using Microsoft Excel.

This micro course is tailored for enthusiasts who want to master data manipulation and visualization with real-time datasets from the Serie A.
Learn to clean, analyze, and visualize player statistics, team performance, and match trends.
Gain the skills to create dynamic dashboards that offer valueable insights.


Excel Bootcamp

  


Serie A Data Lab  

The Intro course is free

  • Video Introduction
  • Paid Section Preview
  • Theory
  • Case Study Preview
  • Practice

#1 Excel Bootcamp

Sections included :

  • Working with tables
  • CountA() Function
  • CountIFS() Function
  • Exploring Excel
  • CountIF() Function
  • SumIF() Function
  • IF() Function
  • Data Validation & CountIF()
  • SumIFS() Function
  • Average() Function
  • AverageIF() Function
  • AverageIFS() Function
  • Pivot Tables

#2 Advanced Excel 

Sections included :

  • VLOOKUP() Function
  • MATCH() Function
  • LEFT() Function
  • HLOOKUP() Function
  • TRIM() Function
  • RIGHT() Function
  • INDEX() Function
  • CLEAN() Function
  • MID() Function
  • SUBSTITUTE() Function
  • Data Cleaning

#3 Playground

Sections included :

  • Case Study 1: The Home Ground Advantage
  • Case Study 2: Clash of the Titans
  • Case Study 3: Win - Loss Ration
  • Case Study 4: Analyzing Data based on Performance Ranking

#4 Practice Section

Sections included :

  • IF() Function Exercises
  • COUNTA(), COUNTIF() & COUNTIFS()
  • SUM(), SUMIF() & SUMIFS() Functions
  • AVERAGE(), AVERAGEIF() & IFS()

Data Labs

#5 Case Studies

A Data Lab is essentially a dynamic learning environment where we put into practice the latest skills we've acquired in Excel through the League of your choosing.

In this section, our focus shifts to the realm of Serie A data.

Data Lab serves as a practical course where we immerse ourselves in hands-on Case Studies, applying our knowledge to gain insights and make data-driven decisions.

With each Case Study, we explore new dimensions of data analysis, allowing us to delve into the latest trends and uncover hidden patterns within the world of Serie A data.

Sections included :

Case Study #1 : Performance statistics of Bologna

Bologna showcases strong offensive capabilities, but there are areas for improvement, particularly in discipline and player health. The team could benefit from a balanced strategy that considers both aggressive play and long-term player well-being. ⚽🔥

Case Study #2 : Performance statistics of Cremonese

Cremonese shows a balanced performance with room for improvement in both offensive and defensive strategies. The focus should be on discipline to reduce yellow and red cards. Offensively, more scoring opportunities need to be converted into goals. Defensively, fine-tuning is needed to excel in specific matchups. This analysis provides valuable insights for upcoming matches and seasons. ⚽🔥

Case Study #3 : Performance statistics of Empoli

Empoli exhibits a balanced performance with strengths in ball control and resilience. However, they may need to focus on disciplinary aspects, as indicated by the high frequency of yellow cards. The pivot tables further underline their offensive prowess and defensive capabilities, but also highlight areas that could be fine-tuned for better performance. 🏆⚽

Case Study #4 : Performance statistics of Fiorentina

Fiorentina shows a balanced approach to their matches, with good ball control and caution in their gameplay. However, there's room for improvement in their offensive strategies, especially considering the number of shots on target. 🎯

The PivotTables provide additional insights into Fiorentina's performance over time and their discipline in different matchups. The team generally maintains good discipline, as indicated by the low count of red cards, but they could work on improving their offensive capabilities and reducing fouls. 📊⚽

Case Study #5 : Performance statistics of Milan

Milan shows strengths in ball possession and one-on-one situations. However, discipline remains a concern, as does their efficiency in goal-scoring. Focusing on these areas could make Milan an even stronger contender in future matches. ⚽🏆

Case Study #6 : Performance statistics of Sassuolo

Based on the analysis, Sassuolo exhibits a balanced performance in various metrics. They excel in successful passes and shots on target but could improve in discipline, as indicated by the average number of yellow cards. The team has a strong physical presence, winning a significant number of duels. However, the assist stats suggest there's room for improvement in creating scoring opportunities. 📊⚽