Skills you'll learn:
Statistical modeling fundamentalsLogistic regression
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.
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
Last Updated January 15, 2025
Prerequisites:
Basic PythonBasic arithmeticElementary algebraNumPy
Beginner
3 months
Last Updated January 15, 2025
Skills you'll learn:
Statistical modeling fundamentals • Logistic regression • Linear regression • Linear functions
Prerequisites:
Basic Python • Basic arithmetic • Elementary algebra
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.
Course 2 • 2 weeks
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 7 • Project
Describe Health and Sleep Quality Data
Describe data related to health and sleep quality. Using the software of your choice to calculate descriptive statistics about a dataset, you’ll report your findings in a slide deck presentation.
Course 3 • 3 weeks
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 dice.
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 6 • Project
Calculate Basketball Scoring Probabilities
Analyze data related to basketball shooting statistics. You’ll use the software of your choice to calculate probabilities related to a dataset, then report your findings in a slide deck presentation.
Course 4 • 4 weeks
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.
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 7 • Project
Analyze A/B Test Results
You will be working to understand the results of an A/B test run by an e-commerce website. Your goal is to work through to help the company understand if they should implement the new page design.
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.
Student Reviews
Average Rating: 5 Stars
1 Reviews
Jon S
July 19, 2023
One of the best nanodegrees that I have found on Udacity.
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Your subscription also includes:
Your subscription also includes:
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Programming for Data Science with R
(51)
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Introduction to Python
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Introduction to Data
1 month
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2 weeks
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3 weeks
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Business Intelligence Analytics
2 months
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Data Scientist
(807)
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Data Analysis and Visualization with Microsoft Power BI
(94)
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4 weeks
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3 weeks
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About Statistics for Data Analysis
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.