# Introductory Statistics for Data Analytics

### Next Offering

**Start Date:** January 6th, 2020**End Date:** February 24th, 2020

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 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.

#### 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:

- 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.

Prerequisites

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

*starting the statistics class.*

__before__- 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

While Introductory Statistics for Data Analytics is required, individuals who have a strong

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

#### Text Book

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.

#### 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.