Sports Analytics in Python

Begin your python journey by learning basic concepts, syntax, and semantics through practical exercises. Install and use Anaconda, and practice all examples to build transferable programming skills.

Learn to download and install Anaconda across Mac, Linux, or PC, launch JupyterLab or Jupyter Notebook in your browser, and create a project folder and Python file to run code.

Learn to write your first Python program by printing hello world, using the print built-in function, adding comments, and understanding common errors in an interpreted language.

Explore Python data types—integers, booleans, floats, and strings—and learn how to inspect types with type(), and convert between int, float, and str using typecasting.

Explore arithmetic expressions in Python by performing addition, subtraction, multiplication, and division, and learn about operator precedence, division methods, modulo, and using variables to store and print results.

Learn how to create and manipulate tuples in Python, including mixed data types, assignment, indexing (positive and negative), slicing, concatenation, sorting with sorted, and nesting.

Learn to create and use dictionaries in Python, access values by keys, and iterate with .keys and .values. Practice adding, deleting, and checking membership with country city mappings.

Learn how to create and manipulate lists in Python using square brackets, indexing (zero-based, positive and negative), and nesting; practice slicing, extending, and appending elements while understanding mutability.

Explore how Python sets enforce uniqueness using curly bracket syntax, create sets from lists, and perform add, remove, intersection, union, difference, and subset or superset checks.

Explore how to use assignment and comparison operators in Python to evaluate conditions, such as equals, not equals, greater than, and less than, with practical examples.

Learn conditional branching in python with if, elif, and else to execute code based on numeric comparisons. Explore practical examples like checking seconds against 60 and qualification logic.

Learn how to use and, or, and not in Python to evaluate multiple conditions, with examples of year ranges, age checks, and color comparisons.

Learn to identify and handle python exceptions, including name error, index error, and zero division error, using try-except and else blocks with practical examples.

Learn how to use built-in and user defined functions in python. Define functions with def, pass arguments, and return results such as length and basic calculations.

Learn how local and global variables work in python, and how scope governs access inside and outside functions, with x, y, and z as examples.

Learn to define functions with unknown numbers of positional arguments using *args and keyword arguments, use for loops to iterate over teams, print results, and return division results.

Master python loops by using for loops with range to print numbers and month indices, then employ while loops that run until a condition is false and increment indices.

Define a class as a blueprint and create objects with an init constructor. Learn to instantiate, initialize attributes, define methods, modify and delete objects in Python.

Learn to manage text files in Python by uploading, opening, reading, writing, and appending content. Also practice deleting files with os.remove and handling errors like no such file or directory.

Explore NumPy, the open-source numerical Python library, to perform array operations and build efficient 1d and multi-dimensional arrays for scientific and engineering calculations.

Create a NumPy array by importing numpy as np, define an array with values zero to four, and access the third element (index two) by printing it.

Practice indexing, slicing, and assigning values on an array to retrieve elements, slice subarrays, and update items, showing how values change after modification.

Explore NumPy arrays, inspect attributes like size and ndim, and compute mean, standard deviation, and max with practical examples in sports analytics in Python.

Learn to create numpy arrays, perform elementwise addition and multiplication, compute the dot product, and use numpy pi and sign to evaluate functions.

Learn to create and convert nested lists to numpy arrays, inspect dimensions, shape, and size, and access elements in a 2d array.

Explore 2d array operations in numpy by adding and multiplying 2d arrays, and computing dot products with numpy's dot function, using x and y examples.

learn how to clean and analyze sports data with pandas, pre-process data, and perform statistical analysis using English Premier League datasets, with introductory visualization via matplotlib and seaborn.

Launch a Jupyter notebook, import pandas and numpy, read the points_table.csv with pandas, inspect with head and shape, and rename or drop columns in place.

Drop rows in pandas to remove teams, then sort by losses or goal difference and create a total goals column from goals for and goals against.

Load a dataset with pandas read_csv, inspect dtypes, and convert the date column from object to datetime using pd.to_datetime, then verify results with head.

Explore descriptive statistics and visualizations for player statistics data, including goals and appearances, using histogram, scatter plots, regression with Seaborn, and Pearson correlation to reveal positive relationships.

Design a real-world Python project to compute Premier League win percentages from 2000–2018 using a prepared dataset, with data prep, goals for and against, and team ranking.

Learn regression analysis on a sports data set to relate goals for to win percentage, using imputation, visualization, and ordinary least squares regression.

Analyze the relationship between player salaries and team performance in a Premier League dataset using regression to assess causality, estimate effects with ordinary least squares, and interpret p-values and R-squared.

Explore scikit-learn in Python by applying logistic regression and support vector machines to an NBA dataset, offering a brief, practical introduction to machine learning concepts.

Apply logistic regression, a supervised learning technique, to predict if a rookie lasts more than five years in NBA dataset, using data cleaning, imputing nulls with mean, and train-test split.

Apply support vector machines to predict game outcomes using a 14,305-row dataset, with an 80/20 train-test split, standard scaling, and a linear SVC classifier, achieving about 65% accuracy.