Statistics for Data Analysis

Nanodegree Program

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.

Beginner

3 months

Real-world Projects

Completion Certificate

Last Updated June 14, 2024

Skills you'll learn:

Outlier analysis • Statistical sampling • Basic probability • Probability distribution

Prerequisites:

Basic Python • Intermediate Python • Basic arithmetic

Courses In This Program

Course 1 • 45 minutes

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!

Lesson 1

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Lesson 1

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 • 5 hours

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.

Lesson 1

Introduction

In this lesson, we kick off the course with an introduction to data and descriptive statistics.

Lesson 2

Data Types

In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.

Lesson 3

Measures of Center

In this lesson, we get into the calculations and use cases for three popular measures of center: mean, median, and mode.

Lesson 4

Notation

In this lesson, we demystify the mathematical notation used for random variables, observed values, and aggregations.

Lesson 5

Measures of Spread

In this lesson, we get into the calculations and use cases for several popular measures of spread, including five-number summaries, standard deviation, and variance.

Lesson 6

Shape and Outliers

In this lesson, we cover two additional forms of descriptive statistics for data distributions: shape and outliers.

Lesson 1

Introduction

In this lesson, we kick off the course with an introduction to data and descriptive statistics.

Lesson 2

Data Types

In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.

Lesson 3

Measures of Center

In this lesson, we get into the calculations and use cases for three popular measures of center: mean, median, and mode.

Lesson 4

Notation

In this lesson, we demystify the mathematical notation used for random variables, observed values, and aggregations.

Lesson 5

Measures of Spread

In this lesson, we get into the calculations and use cases for several popular measures of spread, including five-number summaries, standard deviation, and variance.

Lesson 6

Shape and Outliers

In this lesson, we cover two additional forms of descriptive statistics for data distributions: shape and outliers.

Course 3 • 10 hours

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.

Lesson 1

Introduction

In this lesson, we start off with meeting the instructors and a high-level overview of probability.

Lesson 2

Probability Basics

In this lesson, you'll gain the basics of probability using coins and die.

Lesson 3

Binomial Distribution

In this lesson, you'll learn about one of the most popular distributions in probability - the Binomial Distribution.

Lesson 4

Conditional Probability

Not all events are independent. In this lesson, you'll learn the probability rules for dependent events.

Lesson 5

Bayes Rule

In this lesson, you'll learn one of the most popular rules in all of statistics - the Bayes Rule.

Lesson 1

Introduction

In this lesson, we start off with meeting the instructors and a high-level overview of probability.

Lesson 2

Probability Basics

In this lesson, you'll gain the basics of probability using coins and die.

Lesson 3

Binomial Distribution

In this lesson, you'll learn about one of the most popular distributions in probability - the Binomial Distribution.

Lesson 4

Conditional Probability

Not all events are independent. In this lesson, you'll learn the probability rules for dependent events.

Lesson 5

Bayes Rule

In this lesson, you'll learn one of the most popular rules in all of statistics - the Bayes Rule.

Course 4 • 15 hours

Experimentation

Experimentation 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 experimentation as well as how to implement statistical concepts in Python.

Lesson 1

Introduction

In this lesson, we begin the course by meeting the instructors and giving a quick introduction to experimentation.

Lesson 2

Normal Distribution Theory

In this lesson, you'll learn the mathematics behind moving from a coin flip to a normal distribution.

Lesson 3

Sampling Distributions and the Central Limit Theorem

In this lesson, you'll learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

Lesson 4

Confidence Intervals

In this lesson, you'll learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

Lesson 5

Hypothesis Testing

In this lesson, you'll learn the necessary skills to create and analyze the results of hypothesis testing.

Lesson 6

A/B Tests

In this lesson, you'll work through a case study of how A/B testing works in the context of website metrics for an online education company.

Lesson 1

Introduction

In this lesson, we begin the course by meeting the instructors and giving a quick introduction to experimentation.

Lesson 2

Normal Distribution Theory

In this lesson, you'll learn the mathematics behind moving from a coin flip to a normal distribution.

Lesson 3

Sampling Distributions and the Central Limit Theorem

In this lesson, you'll learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

Lesson 4

Confidence Intervals

In this lesson, you'll learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

Lesson 5

Hypothesis Testing

In this lesson, you'll learn the necessary skills to create and analyze the results of hypothesis testing.

Lesson 6

A/B Tests

In this lesson, you'll work through a case study of how A/B testing works in the context of website metrics for an online education company.

Taught By The Best

Sebastian Thrun

Founder and Executive Chairman, Udacity

As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.