Statistics for Data Analysis

via Udacity Nanodegree

Overview

Students will learn essential skills, including describing data, understanding probability theory, designing experiments, interpreting statistical results, and applying statistical models with Python. After successfully completing this Nanodegree program, graduates will be armed with a robust foundation in statistical analysis that can be applied to Data Analyst, Business Analyst, and Data Scientist roles.

Syllabus

Welcome to the Statistics for Data Analysis Nanodegree Program

Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!

Descriptive Statistics

Learn how to describe data in terms of data types, measures of center, measures of spread, shape, and outliers. These essential skills in descriptive statistics provide the foundation for more advanced statistical techniques that are used for data science, data analysis, and machine learning.

Probability

This course is a comprehensive dive into the fundamental concepts and principles of probability. You’ll begin with basic probability theory, then progress to more complex topics such as binomial distributions, conditional probability, and Bayes’ Rule. These skills will enhance your ability to reason about uncertainty and make claims using data.

Hypothesis Testing

Hypothesis testing is one of the most important topics in all of statistics because it tells us whether our conclusions are statistically significant. In this course, you will learn about the fundamental role statistics plays in hypothesis testing as well as how to implement statistical concepts in Python.

Linear and Logistic Regression

The linear and logistic regression course offers a detailed introduction to fundamental statistical and machine learning algorithms, particularly focusing on regression techniques. The course begins with simple linear regression and progresses to multiple linear regression, equipping students with the ability to analyze relationships between multiple variables. Finally, it covers logistic regression, a powerful tool for classification problems.