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Introductory Statistics for Data Analytics

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Start Date: May 8th, 2019
End Date:   June 26th, 2019

Introductory Statistics for Data Analytics is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. The course is taught by faculty from the department of Statistics at The Ohio State University. The course is delivered in 100% distance learning format and includes instructional material equivalent to a one semester credit hour class.   

This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. It is required that participants take the Introductory Statistics for Data Analytics first. For individuals who have a strong background in Statistics and experience using R Software, it is not required and they can complete three other courses to receive the CPDA certification. It is up to each participant to decide on their own preparation. Contact the program for clarification or questions.

Course Description

Introductory Statistics for Data Analytics includes; a short discussion of where data comes from; data exploration; probability and random variables; the basics of statistical inference (e.g., sampling and inferring upon population parameters using statistics); testing statistical hypotheses and building confidence intervals; and an introduction to regression. Students will use the R software package in this course.

4 CEUs are granted upon successful completion of the course.


You will learn to:
  1. Describe data graphically and numerically using statistical software.
  2. Explain basic concepts of probability and random variables.
  3. Recognize how data can be used to infer features of probability distributions.
  4. Test for consistency of data with particular values of parameters.
  5. Estimate parameter values and quantify uncertainty in the estimate.
  6. Interpret linear association. 

It is strongly recommended that participants have taken at least one course in college level algebra prior to taking this course. 

This class requires you to use the statistical software package called R (The R Project for Statistical Computing;( This software package is available as Free Software.

  • From the CRAN archive at, you can download R for Windows, Mac, and Linux.
  • An in-depth introduction to R is available at
  • Hands-on tutorials are available in the Swirl system, which you can learn about at In particular, “R Programming: The basics of programming in R” is an appropriate first tutorial for students who have never used R.
  • An easier to use interface to R is available in the software package RStudio. This package is available for Windows, Mac, and Linux and can be downloaded for free from


Click Here to learn more about how this course is delivered 100% online!

Text Book

Participants are required to purchase the following book for this course. 
Introduction to the Practice of Statistics, 7th Edition, Moore, McCabe and Craig (2010). ISBN: 978-1429274333, ASIN: 1429274077. Earlier editions of the textbook can be used as well and contain all the same material as the later edition.


Expected Time Commitment to Complete this Course

Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so each week there is 5.7 hours of combined class time (40 hrs / 7 weeks). The average student should allow a 2:1 study-to-class-time ratio to complete the course. This means you should plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs).  Based on a person's own personal strengths and experience, you should increase or decrease the ratio. 

Cancellations and Refunds

A full refund minus a $50 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date. 

Course Offering Dates

Each course offering in this program is faculty lead, therefore it operates with a specific start date and end date. Students must complete each course during the specific time frame. Access to the online course and materials is removed when the course ends.