Math for Data Science
Learning Path ⋅ Skills: Statistics, Correlation, Linear Regression, Logistic Regression
In this learning path, you’ll gain the mathematical foundations you’ll need to get ahead with data science.
Additional Resources
Math for Data Science
Learning Path ⋅ 5 Resources
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.
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.
Tutorial
Stochastic Gradient Descent Algorithm With Python and NumPy
Learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.
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