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*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.
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 and hypothesis testing, including t-tests, chi-square...
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 and hypothesis testing, including t-tests, chi-square tests and ANOVA. The course has no prerequisites, although you may need to spend some extra time learning the basics of R if you haven't used it before.
Each week of Foundations of Data Analysis begins with a reading assignment, a couple of lecture videos with comprehension questions and an R programming tutorial. The videos tend to be in the 7-10 minute range and the tutorials typically total less than 10 minutes a week, so the total video content per week is usually 20-30 minutes. The videos are generally well-edited and the professor does a good job describing concepts simply and concisely. Each week has a prelab, lab and problem set that allow you to apply the concepts you learn in lecture and in the R tutorials. Each problem set consists of 3-4 mini case studies, so you'll probably end up spend most of your time on the labs and problem sets. The assignments are not very difficult, although many questions limit you to 1 or 2 attempts. You need a cumulative score of 70% to earn a certificate.
Foundations of Data Analysis introduces new concepts at a relatively slow pace and gives students a good amount of practice through the labs and assignments. Concepts are explained well in lecture so the readings are not always necessary to do the activities, but they often provide extra depth and raise considerations that are not discussed lecture. The course did have some hiccups with homework questions and auto-graders and many answers expect rounded answers, which can result in frustrating off-by-a-fraction errors. In addition, the course uses an external forum system called Piazza instead of the normal edX forums, which I found to a hassle.
Bottom line: UT.7.01x is a great place for a beginner to start with stats and R as long as you don't mind an external forum.
I give Foundations of Data Analysis 4 out of 5 stars: Very Good.
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 accompanying DASI* (which I just completed). R-wise...
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 accompanying DASI* (which I just completed). R-wise you get:
- a video showing you what to do,
- a pre-lab, essentially providing the code and detailed instrucions, with forgiving grading,
- a lab with leaa detailed instructions and harder grading - one try.
- often a quite briefly worded question based on the same data and type of analysis in the problem set.
This way you get to use the same R commands several times, with increasing independence. Not stressful & the repetition helps you pick up the commands, syntax etc.
MOOC MAKERS: THIS IS HOW INTRO COURSES SHOULD BE DONE.
1) forums are on PIAzza**. Initially I thought this was a pure PIA - EdX *has* forums. I'm now more equivocal: Piazza's forum-wiki hybrid works quite well. But regardless, you'll need to sign up & remember yet another login to use the forums...
2) too much reliance on written materials in some weeks: pdfs of texts from ck12.org, totalled 84 pages in week 2, buta averaged less. Lecture coverage is clear and engaging, but relatively brief & perhaps not enough for someone new to the subject to pick it up from. If you're familiar with the material, much of the reading may be optional. If not, and don't have a convenient/comfortable way to read/take notes (probably a tablet) this course may not be for you.
A Piazza poll indicated the MOOCmates agreed week 2's reading was excessive, but there's was less (~30 pages) set for weeks 3-5
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.