Math for Data Science (Learning Path) – Real Python
Learning Path ⋅ Skills: Statistics, Correlation, Linear Regression, Logistic Regression, NumPy, SciPy, pandas, Gradient Descent
In this learning path, you’ll build the mathematical foundations for data science. You’ll start with statistics fundamentals and correlation analysis using NumPy, SciPy, and pandas, then move on to linear regression, logistic regression, and stochastic gradient descent.
Math for Data Science
Learning Path ⋅ 5 Resources
Statistics and Correlation
Start with the building blocks of data analysis. You’ll learn to describe datasets with statistics and measure relationships between variables using correlation.
Tutorial
Python Statistics Fundamentals: How to Describe Your Data
Learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, pandas, Matplotlib, and the built-in Python statistics library.
Tutorial
NumPy, SciPy, and pandas: Correlation With Python
Learn what correlation is and how you can calculate it with Python. You'll use SciPy, NumPy, and pandas correlation methods to calculate three different correlation coefficients. You'll also see how to visualize data, regression lines, and correlation matrices with Matplotlib.
Regression and Optimization
Learn to model relationships in data with regression techniques. You’ll work through linear regression, logistic regression, and the stochastic gradient descent optimization algorithm.
Course
Starting With Linear Regression in Python
Get started with linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.
Tutorial
Logistic Regression in Python
Get started with logistic regression in Python. Classification is one of the most important areas of machine learning, and logistic regression is one of its basic methods. You'll learn how to create, evaluate, and apply a model to make predictions.
