Introductory Statistics for Data Analytics
Start Date: August 22, 2023
End Date: October 10, 2023
Introductory Statistics for Data Analytics is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. If you pursue the entire certification, this course must be taken first in the sequence.
Statistics is the science of learning from data. In this course students learn how to apply statistical analysis to data. Students also begin learning how to use statistical software that is used in the other courses as well. (R Project for Statistical Computing).
The course is co- taught by three faculty from the department of Statistics at The Ohio State University. The course is delivered in 100% distance learning, asynchronous format and includes instructional material equivalent to a one semester credit hour class.
The Introductory Statistics for Data Analytics course teaches students learn how to apply statistical analysis to data. In doing so, students also begin learning how to use statistical software (R Project for Statistical Computing) for statistical analysis. The course includes these topics; 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.
Students will learn to:
- Describe data graphically and numerically using statistical software.
- Understand basic concepts of probability and random variables.
- Appreciate how data can be used to infer features of probability distributions.
- Test for consistency of data with particular values of parameters.
- Estimate parameter values and quantify uncertainty in the estimate.
- Interpret linear association and can conduct simple linear regression data analysis.
Below is a video of the instructors for this course explaining what students will learn and can expect from this course.
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; (http://www.r-project.org/). This software package is available as Free Software.
Students must complete this free training before starting the statistics class.
- Hands-on tutorials are available in the Swirl system, which you can learn about at http://swirlstats.com/. 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 http://rstudio.org.
- From the CRAN archive at https://cran.r-project.org, you can download R for Windows, Mac, and Linux.
- An in-depth "Introduction to R" training manual is available at http://cran.r-project.org/doc/manuals/R-intro.pdf
It is recommended students 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.
The course is graded pass/fail. Students must receive an 80% or better on all graded items to pass and receive the certificate of completion for the course.
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class with each class consisting of approximately 40 hours of class time (12-13 hours of recorded faculty lectures and 23-24 hours of additional course work). Each course is seven weeks in length. 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. We recommend students 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.