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# UT.7.01x: Foundations of Data Analysis

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## Overview

*Note - This is an Archived course*

In a world that’s full of data, we have many questions: How long do animals in a shelter have to wait until they are adopted? Can we model the growth of internet usage in a country? Do films with a more adult rating make more money that other rated films?

Luckily, the world is also full of data to help us answer those questions. This course will walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.

This course will consist of instructional videos for statistical concepts broken down into manageable chunks – each followed by some guided questions to help your understanding of the topic. Most weeks, the instructional section will be followed by tutorial videos for using R, which we’ll then apply to a hands-on Lab where we will answer a specific question using real-world datasets.

We’ll cover basic Descriptive Statistics in our first “Unit” – learning about visualizing and summarizing data. Unit two will be a “modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We’ll learn how to interpret and use those functions with a little bit of Pre-Calculus (but we’ll keep it very basic). Finally in the third Unit, we’ll learn about Inferential statistical tests such as the t-test, ANOVA, and chi-square.

This course is intended to have the same “punch” as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).

I hope you’ll join me in learning how to look at the world around us – what are the questions? How can we answer them? And what do those answers tell us about the world we live in?

How long is the course?
The course is scheduled to run from November 4, 2014 to February 6, 2015. While this time frame covers 13 weeks, there may be a break from December 22, 2014 to January 1, 2015 to align with the University of Texas at Austin's winter break.

Do I need a Windows PC?
No. A Mac or a PC will work just fine. You’ll need to download both R and RStudio and install them. Both pieces of software have a PC and a Mac version.

Are there any specific technology requirements?
Access to a computer with internet access that you can install software on (R and RStudio). You may also need a calculator.

Is there a text book associated with the course?
Yes and no. The text that we will be using is a custom created open source text that will be embedded into the edX course as PDF readings.

This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people enjoy watching the videos and working with the materials. Make sure to check for reruns of this course.

### Taught by

Michael J. Mahometa

## Reviews

4.6 rating, based on 8 reviews

Start your review of UT.7.01x: Foundations of Data Analysis

• Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be easy.

UT.7.01x: Foundations of Data Analysis is a gentle, 13 week introduction to statistics and the R programming language provided by the UT Austin through the edX MOOC platform. The course covers basic descriptive statistics, the normal distribution, sampling...
• Prose Simian is taking this course right now, spending 4 hours a week on it and found the course difficulty to be easy.

Impressions based on five (of 13) weeks materials: with a couple of caveats, this looks set to be a good intro to statistics, and particularly for getting used to using R for basic data analysis. The labs are lengthier, and more incremental than those...
• William Lucas completed this course, spending 10 hours a week on it and found the course difficulty to be easy.

I majored in Economics in school and currently teach Algebra so I'm biased. I finished this course in approximately twenty hours over the course of a week. The information is elementary and presented in an extremely accessible way. My advice is not to do the textbook readings because the videos present the content much more efficiently and effectively. Do all of the quizzes/checks/etc that you do not understand.

The course presents the material in an engaging fashion, using videos, readings, PDF options, and mini-projects to guide you along. Excellent course.
• Maboroshi completed this course, spending 3 hours a week on it and found the course difficulty to be medium.

An introduction statistics course with hands-on R studio introduction. Very practical, although the lectures are a bit short. It's nicer to get refresh of previous knowledge plus R introduction!
• Rupa Lahiri is taking this course right now, spending 3 hours a week on it and found the course difficulty to be easy.

I keep coming back to the course and revisiting things I have forgotten. Thank you for this course. It is a good place to start.
• Asr completed this course.

• Stojan Karlusic
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Stojan Karlusic completed this course.

• Cristina completed this course.

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