Interpreting Data with R

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Interpreting Data with R

Authors: Justin Flett, Fredrik Hallgren, Emilee McWilliams

Interpreting Data with R is a skill that will teach you how to apply disciplines such as Statistics and Probability to understanding data and preparing future models.

Related topics

Statistics & Algorithms Data Science Feature Engineering Hypothesis Testing Data Modeling

What You Will Learn

  • This skill will convey the most common techniques for using R to help interpret data, both with and without statistical models.

Pre-requisites

  • Data Literacy
  • Data Analytics Literacy
  • R for Data Analysts
  • Statistics and Probability
  • Data Visualization with R

Beginner

Visualize and discover relationships in your data, and calculate correlations and perform simple linear regressions to predict a value from a single predictor.

Finding Relationships in Data with R

by Justin Flett

Jul 26, 2019 / 1h 33m

1h 33m

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Description

Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Finding Relationships in Data with R you will learn foundational knowledge of solving real world data science problems. First, you will learn the basics of discovering and visualizing relationships within data. Next, you will learn how correlation values and correlation matrices can be used to analyze relationships within data. Finally, you will explore how to understand and implement correlation matrices and dataframes using heatmaps and pairs plots. When you’re finished with this course, you will have the skills and knowledge of R needed to discover and understand relationships within data.

Table of contents
  1. Course Overview
  2. Visualizing Relationships within Data
  3. Discovering Relationships within Data
  4. Calculating Correlation between Variables
  5. Creating Correlation Matrices
  6. Visualizing Correlation Matrices using Heatmap
  7. Visualizing Relationships using Pairplot

Interpreting Data Using Statistical Models in R

by Fredrik Hallgren

Oct 8, 2019 / 1h 45m

1h 45m

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Description

We need principles, models, and theory to make sense of the vast amounts of data generated in today’s world. In this course, Interpreting Data Using Statistical Models in R, you will gain the ability to apply statistical and data science models to any task. First, you will learn how to fit statistical models to data. Next, you will discover how to test for relationships in data. Finally, you will explore how to create predictions with linear regression. When you are finished with this course, you will have the skills needed to turn data into knowledge.

Table of contents
  1. Course Overview
  2. Creating Statistical Models
  3. Fitting Statistical Models
  4. Implementing a Predictive Model: Single-variable Linear Regression
  5. Drawing Conclusions from Data with Statistical Testing
  6. Using Multi-variable Linear Regression
  7. Ensuring Predictive Accuracy

Intermediate

Calculate common descriptive statistics for central tendency and variability as well as summarize a given set of data using appropriate descriptive statistics.

Interpreting Data Using Descriptive Statistics with R

by Emilee McWilliams

Aug 13, 2019 / 1h 24m

1h 24m

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Description

Interpreting statistics can be confusing or time consuming. In this course, Interpreting Data using Descriptive Statistics with R, you will learn foundational knowledge to efficiently describe a data set using R. First, you will learn how to calculate mean, median, and mode. Next, you will discover variance and standard deviation. Finally, you will explore how to compare datasets using these statistics. When you’re finished with this course, you will have the skills and knowledge of computing these statistics needed to explain a dataset.

Table of contents
  1. Course Overview
  2. Understanding Types of Variables and Measurement Scales
  3. Interpreting Measures of Location within R
  4. Measuring Variance with Descriptive Statistics
  5. Creating Credit Application Summary for Month over Month Data
  6. Applying Standard Error and Confidence Estimates to a Sample Mean
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