Math for Data Science
Learning Path ⋅ Skills: Statistics, Correlation, Linear Regression, Logistic Regression, NumPy, SciPy, pandas, Gradient Descent
The math behind data science becomes concrete when you implement it in Python. This learning path walks you through the key mathematical concepts that power data science workflows.
By completing this path, you’ll be able to:
- Calculate descriptive statistics and probability distributions with Python
- Measure and interpret correlation using NumPy, SciPy, and pandas
- Build and evaluate linear regression models
- Implement logistic regression for classification problems
- Apply the stochastic gradient descent algorithm with NumPy
This path is for Python developers who want to understand the math driving their data models. Familiarity with Python and basic algebra is recommended.
You’ll start with statistics and correlation, then progress to regression techniques and optimization.
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.
Interactive Quiz
Linear Regression in Python
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.
Congratulations on completing this learning path! You’ve built a solid mathematical foundation for data science with Python.
You might also be interested in these related learning paths:
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