Sports Analytics In Python

The Complete Sports Analytics Course: Python, Numpy, Pandas, Data Analysis, Data Visualization, Machine Learning

What you'll learn

Collect and manipulate sports data using Python libraries such as Pandas and NumPy

Visualize and explore data using Matplotlib and Seaborn

Apply statistical techniques to analyze player and team performance

Build predictive models to forecast game outcomes

Requirements

No programming experience need. You will learn all the things you need in this course

Description

Welcome to the Sports Analytics in Python course on Udemy! In this course, you will learn how to apply the power of Python programming to sports analytics.Sports analytics has become an increasingly important field in recent years, as teams, athletes, and analysts seek to gain a competitive edge through data-driven insights. In this course, you will learn how to use Python programming to explore and analyze sports data, including data on athlete performance, team statistics, and league trends.Throughout the course, you will work on a series of hands-on projects, starting with the basics of Python programming and data analysis, and gradually building up to more advanced techniques. You will learn how to clean and manipulate data, visualize data using Python's powerful data visualization libraries, and perform statistical analysis on sports data.By the end of the course, you will have gained a solid foundation in Python programming and sports analytics, and you will be able to apply your new skills to a wide range of real-world problems. Whether you are a sports enthusiast, a data analyst, or simply looking to expand your programming skills, this course is designed to help you achieve your goals.Join us today and discover the power of Python programming for sports analytics!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python

Lecture 2 Python Introduction

Lecture 3 Downloading and Installing Anaconda

Lecture 4 Writing your first python program

Lecture 5 Data Types in python

Lecture 6 Arithmetic Expressions in python

Lecture 7 Tuples in python

Lecture 8 Strings in python

Lecture 9 Dictionaries in python

Lecture 10 List in python

Lecture 11 Sets in python

Lecture 12 Arithmetic Operators in python

Lecture 13 Conditional branching in python

Lecture 14 Logical Operators in python

Lecture 15 Exceptions in python

Lecture 16 Functions in python

Lecture 17 Function Vriables in python

Lecture 18 Function Arguments in python

Lecture 19 Loops in python

Lecture 20 Classes and Objects

Lecture 21 Files in python

Section 3: Numpy

Lecture 22 Numpy Introduction

Lecture 23 Creating an array

Lecture 24 Array operations

Lecture 25 Array attributes size and dimension

Lecture 26 Array addition and multiplication

Lecture 27 2D Arrays

Lecture 28 2D Array operations

Section 4: Data Analysis with Pandas

Lecture 29 Pandas Introduction

Lecture 30 Accessing data in pandas

Lecture 31 Dropping rows in pandas

Lecture 32 Working with Date and Time in pandas

Lecture 33 Summary Statistics

Lecture 34 Project 1

Lecture 35 Regression Analysis

Lecture 36 Project 2

Section 5: Machine Learning with Scikit learn

Lecture 37 Scikit Learn Introduction

Lecture 38 Logistic Regression

Lecture 39 Support Vector Machines

Whether you are a sports enthusiast, a data analyst, or simply looking to expand your programming skills, this course is designed to help you achieve your goals.