EdX

Foundations of Data Analysis - Part 1: Statistics Using R (edX)

Foundations of Data Analysis - Part 1: Statistics Using R (edX)

Use R to learn fundamental statistical topics such as descriptive statistics and modeling. 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.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

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.

In this first of a two part course, 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).
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?
Want to learn more? Be sure to enroll in Foundations of Data Analysis - Part 2, starting March 22, 2016.
What you'll learn:

  • Descriptive Statistics
  • How to visualize data
  • Data structure and how to examine it
  • Basic R programming (guided through tutorials)
  • Simple modeling of linear, exponential, and logistic growth
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Probability and Statistics II: Random Variables – Great Expectations to Bell Curves (edX) EdX
Georgia Institute of Technology,GTx

Probability and Statistics II: Random Variables – Great Expectations to Bell Curves (edX)

This course discusses properties and applications of random variables. For instance, how many customers are likely to arrive in the next hour? What’s the probability that a lightbulb will last more than a year? When you’re done with this course, you’ll have enough firepower to undertake a wide variety of modeling and analysis problems; and you’ll be well-prepared for the upcoming Statistics courses.

Self Paced
Self-Paced
How to analyze a microbiome (edX) EdX
KU Leuven University,KULeuvenX

How to analyze a microbiome (edX)

Learn common analysis techniques to make sense of microbial sequencing data. Microorganisms play a major role in the biosphere and within our bodies, but only a tiny fraction has been cultured so far. Microbiome data, that is the genetic information of microorganisms, is therefore an important window into the hidden microbial world.

Self Paced
Self-Paced
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced
Case Studies in Functional Genomics (edX) EdX
HarvardX,Harvard University

Case Studies in Functional Genomics (edX)

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor. We will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions.

Self Paced
Self-Paced
High-Dimensional Data Analysis (edX) EdX
HarvardX,Harvard University

High-Dimensional Data Analysis (edX)

A focus on several techniques that are widely used in the analysis of high-dimensional data. If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis.

Self Paced
Self-Paced
Estadística Aplicada a los Negocios (edX) EdX
Galileo University,GalileoX

Estadística Aplicada a los Negocios (edX)

Aprende las principales herramientas y técnicas de la estadística descriptiva y la estadística inferencial para analizar e interpretar datos desde la perspectiva de negocios facilitando la toma de decisiones. Este curso proporciona una introducción al análisis de datos en base a las principales herramientas estadísticas, enfocándose en la estadística descriptiva y la estadística inferencial.

Self Paced
Self-Paced
Excel for Everyone: Core Foundations (edX) EdX
The University of British Columbia,UBCx

Excel for Everyone: Core Foundations (edX)

Learn Excel fundamentals including data wrangling, spreadsheet management, and basic data analysis. This introductory Excel course will equip you with a strong foundational knowledge of Excel to organize, analyze and work with data. You will develop essential Excel skills, such as simple data wrangling and managing spreadsheets, along with a foundational understanding of business data analysis.

Self Paced
Self-Paced
MathTrackX: Statistics (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Statistics (edX)

Understand fundamental concepts relating to statistical inference and how they can be applied to solve real world problems. This course will build on probability and random variable knowledge gained from previous courses in the MathTrackX XSeries with the study of statistical inference, one of the most important parts of statistics.

Self Paced
Self-Paced
Statistical Inference and Modeling for High-throughput Experiments (edX) EdX
HarvardX,Harvard University

Statistical Inference and Modeling for High-throughput Experiments (edX)

A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

Self Paced
Self-Paced