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Statistics for Data Science


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.

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  • 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
  • Mode

What You Will Learn

  1. 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.

      • Binomial Distribution

        Learn about binomial distribution where each observation represents one of two outcomes and derive the probability of a binomial distribution.

        • Bayes Rule

          Build on conditional probability principles to understand the Bayes rule and derive the Bayes theorem.

          • 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.

            • Hypothesis Testing

              Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.

              • 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.

                • Logistic Regression

                  Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.

                  • 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.

                  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.

                  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.

                  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


                    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.

                  Statistics for Data Science

                  Get started today

                    • Learn

                      Key concepts and techniques of inferential statistics. Use them to tackle real-world challenges, such as analyzing AB tests and building regression models.

                    • Average Time

                      On average, successful students take 1 month to complete this program.

                    • Benefits include

                      • Real-world projects from industry experts
                      • Real-time support

                    Program Details

                    • 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

                    Enroll Now