Do I need to apply? What are the admission criteria?

No. This Course accepts all applicants regardless of experience and specific background.

What are the prerequisites for enrollment?

In order to succeed in this program, we recommend having experience working with SQL and with data in Python, ideally with the NumPy and/or pandas libraries.

How is this course structured?

The Statistics for Data Science course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 1 month. The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

How long is this course?

Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.

Can I switch my start date? Can I get a refund?

Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.

What software and versions will I need in this course?

You will need access to the Internet, and a 64 bit computer. Additional software such as Python and its common data analysis libraries (e.g., Numpy and Pandas) will be required, but the program will guide students on how to download once the course has begun.

Statistics for Data Science

Course

Deepen your analytical skills with this beginner-friendly course in real-world statistics. This course will teach you the statistical concepts & techniques you need to conduct rigorous inferential analyses and draw accurate conclusions from data sets.

Enroll Now

Estimated time
1 month
Enroll by
June 14, 2023
Get access to classroom immediately on enrollment
Skills acquired
Sample Testing, Conditional Probability, Linear Regression

In collaboration with

What You Will Learn

Statistics for Data Science
1 month to complete
A solid foundation in statistics is essential to making sense of data in any field, but most courses focus on theory, rather than modern use cases. This course is designed to cut through the noise and teach you the concepts and techniques you need to know to tackle common real-world challenges, such as analyzing AB tests and building regression models.
Prerequisite knowledge
Python & SQL.
1. Simpson’s Paradox
  Examine a case study to learn about Simpson’s Paradox.
2. Binomial Distribution
  Learn about binomial distribution where each observation represents one of two outcomes and derive the probability of a binomial distribution.
3. Bayes Rule
  Build on conditional probability principles to understand the Bayes rule and derive the Bayes theorem.
4. Sampling Distributions and Central Limit Theorem
  Use normal distributions to compute probabilities and the Z-table to look up the proportions of observations above, below or in between values.
5. Hypothesis Testing
  Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.
6. T-Tests and A/B Tests
  Test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.
7. Logistic Regression
  Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.
8. Course Project: Analyze A/B Test Results
  In this project, you will be provided a dataset reflecting data collected from an experiment. You’ll use statistical techniques to answer questions about the data and report your conclusions and recommendations in a report.
- 59%
  of companies plan to increase positions requiring data analysis skills.

All Our Courses Include

Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Real-time support
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Workspaces
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

Course offerings

Class content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Student community
- Real-time support

Succeed with personalized services.

We provide services customized for your needs at every step of your learning journey to ensure your success.

Experienced Project Reviewers
Real-Time Support

Get timely feedback on your projects.

Personalized feedback
Unlimited submissions and feedback loops
Practical tips and industry best practices
Additional suggested resources to improve

1,400+
project reviewers
2.7M
projects reviewed
88/100
reviewer rating
1.1 hours
avg project review turnaround time

Learn with the best.

Josh Bernhard
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
Sebastian Thrun
Instructor
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
Derek Steer
CEO at Mode
Derek is the CEO of Mode Analytics. He developed an analytical foundation at Facebook and Yammer and is passionate about sharing it with future analysts. He authored SQL School and is a mentor at Insight Data Science.
Juno Lee
Curriculum Lead at Udacity
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
Mike Yi
Data Analyst Instructor
Mike is a content developer with a multidisciplinary academic background, including math, statistics, physics, and psychology. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.
David Venturi
Data Analyst Instructor
Formerly a chemical engineer and data analyst, David created a personalized data science master's program using online resources. He has studied hundreds of online courses and is excited to bring the best to Udacity students.
Sam Nelson
Product Lead
Sam is the Product Lead for Udacity’s Data Analyst, Business Analyst, and Data Foundations programs. He’s worked as an analytics consultant on projects in several industries, and is passionate about helping others improve their data skills.