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

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What You Will Learn

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.
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.

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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.