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Online Course

Foundations of Data Analysis - Part 1: Statistics Using R

The University of Texas at Austin via edX

Overview

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

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Reviews

4.0 rating, based on 7 reviews

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

  • Anonymous

    Anonymous completed this course.

    I completed this course under the Audit track, even though I didn't bother with a certificate. This is an excellent course. It teaches not only statistics in a clear, easily understood manner, but also cultivates in the student a structured and methodical...
  • Riham Soliman completed this course, spending 10 hours a week on it and found the course difficulty to be medium.

    This course is a great start to learn both statistics and the basics of R. There are different types of material for different types of learners (course videos, downloadable reading material, tutorial videos) although it is better to use them all while...
  • Zeeshan Mulla completed this course, spending 7 hours a week on it and found the course difficulty to be medium.

    Right one to get started with stats in R. Best to enroll this one (part-1) first and go for the next part Inferential Statistics (part-2).
  • Andrew Nakamura

    Andrew Nakamura completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

    This course ended in Aug 2017. With that said, the prelab and lab units no longer function to provide answers to double check your understanding so what's the point with continuing with the course. I was taking this due to the basic understanding that I'll need some statistic background with R and Python as an aspiring data scientist. This course was highly recommended as the choice class to take as the top pick from Class-Central.

    Unfortunately, someone forgot to tell them that this course is not worth the value without the videos. I'm sure it was a great course, but with all that time and effort, I guess its no longer worth it to maintain courses that can't provide a university with monetary value.
  • Reza Fazeli completed this course.

  • Dennis B. Mendiola

    Dennis B. Mendiola completed this course.

  • Gaute Friis is taking this course right now.

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