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The University of Texas at Austin

Foundations of Data Analysis - Part 1: Statistics Using R

The University of Texas at Austin via edX

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Overview

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In this first part of a two part course, we’ll 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 topics
  • Guided questions to help your understanding of the topic
  • Weekly tutorial videos for using R Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets
  • Weekly wrap-up questions challenging both topic and application knowledge

We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap.

Both parts of the course are intended to cover the same material 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).

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Join us 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?

Syllabus

Week One: Introduction to Data
  • Why study statistics?
  • Variables and data
  • Getting to know R and RStudio
Week Two: Univariate Descriptive Statistics
  • Graphs and distribution shapes
  • Measures of center and spread
  • The Normal distribution
  • Z-scores 
Week Three: Bivariate Distributions
  • The scatterplot
  • Correlation
Week Four: Bivariate Distributions (Categorical Data)
  • Contingency tables
  • Conditional probability
  • Examining independence
Week Five: Linear Functions
  • What is a function?
  • Least squares
  • The Linear function – regression 
Week Six: Exponential and Logistic Function Models
  • Exponential data
  • Logs
  • The Logistic function model
  • Picking a good mode

Taught by

Michael J. Mahometa

Reviews

4.6 rating, based on 8 Class Central reviews

Start your review of Foundations of Data Analysis - Part 1: Statistics Using R

  • 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, s…
  • 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 tho…
  • 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.
  • 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!
  • 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.
  • Stojan Karlusic
    1

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