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Practical Statistics

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.

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04Days06Hrs56Min24Sec

  • Estimated time
    35 hours

  • Enroll by
    September 28, 2022

    Get access to classroom immediately on enrollment

  • Prerequisites
    Python & SQL
In collaboration with
  • Mode

What You Will Learn

  1. Practical Statistics

    35 hours 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

    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.

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                  All our programs 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.

                  • Technical mentor support

                    Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.

                  • 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

                    • Technical mentor support
                    • Student community

                  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

                  Mentors available to answer your questions.

                  • Support for all your technical questions
                  • Questions answered quickly by our team of technical mentors
                  • 1,400+

                    technical mentors

                  • 0.85 hours

                    median response 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

                    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.

                  Practical Statistics

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                  • Monthly access

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                    • Maximum flexibility to learn at your own pace.
                    • Cancel anytime.
                  • 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 35 hours to complete this program.
                  • Benefits include

                    • Real-world projects from industry experts
                    • Technical mentor support

                  Program Details

                  • Do I need to apply? What are the admission criteria?
                  • What are the prerequisites for enrollment?
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                  • What software and versions will I need in this course?

                  Practical Statistics

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