Python - Serie A

Embark on a journey of discovery with Python programming and Serie A data.

This micro course introduces you to data manipulation, visualization, and basic machine learning tailored for the tournament.
Explore player performance predictions, match outcome forecasts, and more.
By mastering Python, you'll equip yourself to unravel meaningful insights that influence game strategies and contribute to the rich tapestry of Serie A history

Python Bootcamp


Serie A Data Lab  

The Intro course is free

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

#1 HEX Installation Guide

Sections included :

  • The benefits of using HEX in Data Analysis
  • HEX Sign Up guide with video and step by step instructions
  • HEX Data Connect Data Guide with video and step by step instructions

#2 Introduction to Python

  Introduction to Python
 Python in the world of Sports Analytics
 Terms you should know
 HEX Installation Guide
 Python Basics
 Data Types
 Python Input
 Python Output
 Python Mathematics
 Python Mathematic Operations
 Python Strings
 Escape Characters
 Practical Examples

#3 Python Data Structures

 Python Data Structures
 Python Lists
 Python Tuples
 Python Sets
 Python Dictionaries
 Practical Examples

#4 Python Functions

 Python Functions
 Defining & Calling a Function
 Function Arguements
 Return Values
 Scope of Variables
 Lambda Functions
 Built-in Functions
 Practical Examples
 Coding Assessment 

#5 Python Modules

 Python Modules
 random - Module
 math - Module
 Python Counter
 Practical Examples

#6 Python Conditionals

 Python Conditionals
 Comparison Operators
 Logical Operators
 Conditional Statements
 Practical Examples
 Coding Assessment

#7 Python Loops

 For Loops
 While Loops
 For vs While Loops
 While & For Loops in Conjunction
 Coding Assessment on For Loops
 Coding Assessment on While Loops

#8 Python Regular Expressions

 Python Regular Expressions Function
 re.match() Function
 re.findall() Function
 re.finditer() Function
 re.sub() Function
 re.split() Function
 Practical Examples
 Coding Assessment

#9 Overall Python Assignment

 Coding Assessment

#10 Optional Content - Practicing with Python Locally

 Local Python Installation
 PyCharm Installation

Part 2 - Python for Data Analytics

The Intro Course is Free

Elevate your knowledge and sharpen your insights with our specially curated Case Studies! Dive deep into the league of your preference and uncover strategies, trends, and data-driven techniques that will propel you to the next level!

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

Fully Interactive

#2 Introduction to Python Data Analysis & Numpy

  Introduction to Python Sports Data Analysis
 NumPy - Arrays
 NumPy - Indexing and Slicing
 NumPy - Broadcasting
 NumPy - Mathematical Functions
 NumPy - Linear Algebra

#3 Pandas

 Introduction to Pandas
 Pandas Series
 Pandas DataFrames
 Data Inspection and Exploration
 Data Cleaning
 Handling Missing Values
 Removing Duplicates
 Sorting & Ranking Data
 Applying Functions
 Grouping Data
 Merging Joining & Reshaping
 Data Grouping & Aggregation
 Timeseries Data
 Categorical Data
 Practical Examples
  Coding Assessment 

#4 Matplotlib

 Basics of Matplotlib
 Line Plots
 Scatter Plots
 Bar Plots
 Pie Plots
 Titles, Labels & Legends
 Colors, Markers & Line Styles
 Ticks, Tick, Labels & Limits
 Histograms and Box Plots
 Heatmaps and Contour Plots
 3D Plots
 Plot Style & Customization
 Grid and Plot Layout
 Subplots, Sharing Axes & Spacing
 Pandas & Matplotlib
 Practical Examples

#5 Seaborn

 Basics of Seaborn
 Basic Plotting
 Box, Violin & Swarm Plots
 FacetGrids, PairGrids and more
 Heatmaps and Clustermaps
 Regression and Line Plots
 Practical Examples

#6 Plotly

 Basics of Plotly
 Graph Objects & Express
 Line, Bar & Pie Charts
 Histograms and Box Plots
 Scatter Plots and Bubble Charts
 Geographic Plots
 Practical Examples

#7 SciPy

 Data Manipulation with Scipy
 Basics of Statistical Analysis
 Hypothesis Testing
 Types of t-tests
 Analysis of Variance
 Linear Algebra
 SciPy Matrix Operations
 Eigenvalues and Eigenvectors
 Linear Equations
 Advanced Aspects of SciPy
 Fourier Transform
 Spatial Data and Distance

Data Labs

#8 Case Studies

A Data Lab is essentially a dynamic learning environment where we put into practice the latest skills we've acquired in Python 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.

Case Study #1: Fiorentina Performance

In this Python Data case study, we embark on an exciting journey through Serie A 2023, where we explore and analyze the performances Fiorentina over the season. 
Join us as we dive into the world of football data, using Python queries to unravel the secrets behind each team's success. 🔍

Case Study #2: Lazio vs Roma

📊📈🔍 In this case study, we'll use data analysis tools to explore the Lazio vs Roma football match.
Discover team strategies, individual performances, positional influences, and match dynamics, as we delve into the treasure trove of raw data! 🧠💻⚽🔥

Case Study #3: Goal-Scoring Patterns

In this case study, we thoroughly investigate a detailed dataset reflecting the goals scored in the "Juventus vs Napoli" match of Serie A 2023. Leveraging Python's powerful data analysis libraries, we formulate probing queries to expose critical insights about the goal-scoring trends and their implications within the match.
Through our analysis, we aim to answer questions concerning goal distribution, types of goals, decisive goal scorers, and more. This exploration empowers us to achieve a comprehensive understanding of the intricate dynamics behind the goals scored in this highly competitive match. 📈

Case Study #4: Defensive Performance

The Serie a is recognized for featuring some of the most thrilling offensive prowess in global football. Yet, behind each triumphant team stands a robust defensive backbone. 
In this Python data case study, we turn our attention to the defensive aspect of the game, diving into the dataset to investigate and analyze defensive performance in the Premier League.
Using Python's powerful data analysis tools, we'll transform raw data into comprehensive insights. 🔍

Case Study #5: Offensive Performance

Offensive Performance represent some of the most electrifying moments in football, with team fortunes often hanging in the balance.
In this Python-based case study, we dive into the penalty shootout data from Serie A, striving to unearth trends and insights related to these tense circumstances.
By evaluating aspects such as success rates, goalkeeper performances, and the influence of the penalty shootout sequence, we aim to achieve an enriched understanding of the dynamics of penalty shootouts in one of the world's most prestigious football tournaments. 🐍🔍

Case Study #6: Season Performance

In our case study today, we don the hat of a football analyst for one of the most successful teams in recent years, Lazio. As part of this journey, we will utilize Python, one of the most versatile programming languages in the world, to conduct an in-depth analysis of Lazio's performance in the 2022-2023 La Liga season.
We will do this through a combination of data exploration, visualization using the interactive plotting library - Plotly, and statistical analysis using Scipy.

Case Study #7: Player Season Performance

🌟🎉 Ladies and gentlemen! Welcome to a deep dive into the world of football statistics and data analytics. Today, we will be exploring the performance of one of the most spectacular football players in the world, Di Maria of Benfica, in the Serie A 2022-2023 season. ⚽🏆
Maria, a truly versatile forward and Benfica's talisman, has mesmerized fans all over the globe with his scintillating performances. As data analysts, we have the privilege of understanding and illustrating his magic in numbers and plots. 📊📈

#9 Optional Content - Practicing with Python Locally

 Local Python Installation
 PyCharm Installation