Beginner

Approx. {{courseState.expectedDuration}} {{courseState.expectedDurationUnit}}

Assumes 6hr/wk (work at your own pace)

Built by
Join {{50879 | number:0}} Students
view course trailer
View Trailer

Course Summary

NOTE: This course has been divided into two courses: Descriptive and Inferential Statistics. If you are new to statistics, we recommend taking these courses instead.

We live in a time of unprecedented access to information...data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

Why Take This Course?

  • The applications of statistics to everyday life
  • Methods for acquiring data through observation and experimentation
  • To organize and describe quantitative and categorical forms of data
  • Anticipating patterns using basic probability and sampling
  • Statistical inference through estimation and hypothesis testing
  • Correlation and simple regression
  • Ways of describing the strength of relationships between variables

Prerequisites and Requirements

It sounds strange to say, but math is not the focus of this class. To do well, however, it is necessary to have a basic understanding of proportions (fractions, decimals, and percentages), negative numbers, basic algebra (solving equations), and exponents and square roots.

See the Technology Requirements for using Udacity.

What Will I Learn?

Syllabus

Module 1: Introduction to Statistics and Methods

  • Lesson 1: Intro to statistical research methods
  • Lesson 2: Frequency Distributions & Visualizing data

Module 2: Describing Data

  • Lesson 3: Central Tendency
  • Lesson 4: Variability
  • Midterm 1 on Lessons 1-4

Module 3: Normal Distribution Analysis

  • Lesson 5: Standardized Scores (z-scores)
  • Lesson 6: Probability and the Normal Distribution
  • Lesson 7: Sampling Distributions

Module 4: Foundations of Inferential Statistics

  • Lesson 8: Estimation
  • Lesson 9: Hypothesis Testing

Midterm 2 on Lessons 5-9

Module 5: Comparing Means

  • Lesson 10-11: t-tests
  • Lesson 12-13: One-way ANOVA

Module 6: Correlation, Regression, and Non-Parametrics

  • Lesson 14: Correlation
  • Lesson 15: Regression (available soon)
  • Lesson 16: Chi-Squared Tests (available soon)

Final Exam on Lessons 10-16 (available soon)

Instructors & Partners

Sean Laraway has taught at San Jose State University since 2004. He completed a post-doctoral fellowship in Psychopharmacology. He has taught statistics at the undergraduate and graduate levels since 1998. He earned his MA and PhD in Behavior Analysis from Western Michigan University in 2003.

Ron Rogers has been a professor of psychology at San Jose State University since 1999. Dr. Rogers has developed and taught statistics and research methods courses at both the undergraduate and graduate level. He earned his M.A. and Ph.D. in Behavioral Neuroscience from Rutgers University in 1995.

instructor photo

Katie Kormanik

Course Developer

After creating the statistics course with Udacity, Katie worked as an instructional designer at Stanford Graduate School of Business and then at McKinsey & Company. She has also consulted for a number of organizations on product development, curricula, and pedagogy. Katie holds Bachelor's Degrees in Mathematics and Economics from the University of Utah and a Master's Degree in International Comparative Education from Stanford University. She loves yoga, snowboarding, singing, reading, and traveling, and has played chess her whole life.