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Enrollment Open!
Introductory Statistics for Data Analysis
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Developed by Dr. Rebecca Barter

When conducting modern analysis of real-world data, understanding statistical methods is crucial. In this course you will learn both traditional statistical techniques as well as modern computational approaches to conducting statistical inference for real data, using the R programming language. This course will empower you to apply statistical methods to real data problems, transforming how you analyze and interpret data.
Open Enrollment
Self-Paced
4 Skills Developed
Online, Self-Paced
$149
Enroll Now
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What you'll learn

1
You want to enhance your ability to make data-driven decisions by implementing both classical and modern versions of statistical techniques, including hypothesis testing, confidence intervals, and linear regression
2
You seek to bridge the gap between theoretical statistical knowledge and practical applications of statistics in R, gaining hands-on experience with real-world data
3
You are eager to refine your statistical intuition, building the confidence to generate and interpret trustworthy statistical results

Supported Careers

Get ready to excel in these roles after taking this program

  • Data Scientist
  • Data Analyst
  • Business Analyst
  • ...and more
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Includes

4 skills developed
Hands-on projects to apply learnings
Project visibility to hiring managers
Lifetime access to course materials
Real feedback from moderators
Insights into your skill gaps
Portfolio artifacts to keep forever

Skills Developed

Some of the exciting skills you will develop through the experience

  • Inference Fundamentals
  • Confidence Intervals
  • Hypothesis Testing
  • Linear Regression

About the skill experiences

Apply common statistical techniques

Create confidence intervals, conduct hypothesis tests, and perform linear regression to answer questions involving real data problems

Conduct statistical analyses in R

Leverage the R programming language to apply statistical techniques

Use both classical and modern statistical inference methods

Use both classical distributional assumptions and modern sampling and bootstrap computational techniques to conduct statistical inference

Theoretical intuition

Understand the theoretical foundations and assumptions of common statistical techniques

Become visible to Hiring Managers with Headlamp

This course lets you build portfolio artifacts alongside our expert instructors. Headlamp shares your portfolio with Hiring Managers looking for your new skill.
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About the Skill Experience Developers:

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Dr. Rebecca Barter

Dr Barter is a Research Assistant Professor at the University of Utah. She is a programmer, data scientist, and statistician with over a decade of experience practicing and teaching data science. She is the co-author of the book “Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making” (www.vdsbook.com) and the author of a popular data science blog (www.rebeccabarter.com).

How Headlamp Works
    Master a most-wanted skill
    Dive into a skill that’s at the top of every Employer’s list. Headlamp makes learning easy with interactive modules and 100% human-support.
    Build a portfolio
    Stand out by documenting your journey through any path. We’ll help you craft a portfolio alongside our expert instructors.
    Get matched with Employers
    Headlamp showcases your portfolio to eager Employers, making entry into the job market as smooth as possible.