Exploratory Data Analysis with R

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Exploratory Data Analysis with R

Authors: Martin Burger, Jacey Heuer

The Exploratory Data Analysis skill teaches you how to probe and learn about a data set using R.

What You Will Learn

  • This skill conveys the most commonly used techniques for EDA using the R programming language and packages.

Pre-requisites

  • Data Literacy
  • Mathematics
  • R for Data Analysts
  • Visualizing Data with R
  • Importing Data with R

Beginner

Learn how Exploratory Data Analysis helps an analyst understand complex data sets and use multiple types of visual exploratory techniques to answer research questions.

Exploring Your First Data Set with R

by Martin Burger

Nov 26, 2019 / 2h 5m

2h 5m

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Description

Do you want to learn how data exploration can be implemented in R? Without data exploration, the whole data analysis process gets inefficient and slow, but follow a good data exploration process and you'll be guided to valuable insights. In this course, Exploring Your First Data Set with R, you will learn how new datasets are explored and analyzed in a quick and efficient way. First, you will learn the methods outlined, following a logical succession, which are applicable in most standard data frames. Then, you will discover how the process is divided into 3 steps: summary statistics, distribution checks, and relation analysis. These steps build on each other and you will find out which variables are worth further analysis and where variable dependencies exist. Finally, you will gain the knowledge of the ground work for machine learning and final data presentation.

When you’re finished with this course, you’ll have the skills to properly structure and conduct data exploration in R.

Table of contents
  1. Course Overview
  2. Background on Exploratory Data Analysis
  3. First Level Data Exploration
  4. Statistical Tests to Confirm Initial Findings
  5. Looking Ahead and Summary

Exploring Data Visually with R

by Martin Burger

Oct 7, 2019 / 2h 1m

2h 1m

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Description

R is very versatile when it comes to data exploration. Any type of chart can be implemented in R. In this course, Exploring Data Visually with R, you will learn the standard visualization techniques that are used for data exploration. First, you will investigate how to use data visualizations. Then, you will explore common datasets with the 4 plot types including scatterplots, barcharts, histograms, and boxplots. Finally, you will be able to use and compare the 3 charting systems, which are R base, lattice, and ggplot2. When you are finished with this course, you will have the skills and knowledge to identify the most suitable chart type and to implement it in 3 different R charting tools.

Table of contents
  1. Course Overview
  2. Background on Data Visualization in R
  3. Data Visualizations Based on Distributions
  4. Data Visualizations Based on Clusters and Grouping Variables
  5. Looking Ahead and Further Resources

Intermediate

Use multiple types of quantitative exploratory, summary and descriptive, and sampling techniques to mine data and answer Exploratory Data Analysis research questions.

Exploring Data with Quantitative Techniques Using R

by Martin Burger

Mar 3, 2020 / 2h 2m

2h 2m

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Description

Do you want to perform data exploration on a large dataset? In this course, Exploring Data with Quantitative Techniques Using R, you will see why R is a great tool in getting to know your data. The course uses a 3 step approach to explore the NYC flights dataset. First, you will get an initial idea via summary statistics. Then, you will use hypothesis tests and visualizations to work on single variables. Finally, you will use techniques for correlations between multiple variables. On top of that, the course also has a module on data sampling which is especially useful for large datasets. When you are finished with this course, you will have the skills and knowledge of data exploration needed to understand a new dataset. You will also use some outstanding add-on packages for the topic.

Table of contents
  1. Course Overview
  2. Understanding Data Exploration
  3. Sampling a Dataset for Data Exploration
  4. Summarizing Data to Get an Understanding of New Data
  5. Using Correlation Analysis

Designing an Exploratory Data Analysis Research Plan

by Jacey Heuer

Jan 23, 2020 / 1h 41m

1h 41m

Start Course
Description

Creating a successful data science project that provides value to an organization can be a complex process. Developing an exploratory data analysis research plan elevates your data science project to an invaluable piece of information for the business. In this course, Designing an Exploratory Data Analysis Research Plan, you will learn what the components of a research plan are and strategies to implement the insights found into the organization. First, you will explore the basic components of a research plan and the difference between an academic and business-oriented exploratory data analysis research plan. Next, you will learn how to ask the right questions and begin getting a grasp on your data. Finally, you will develop a set of insights, including a series of machine learning models, and deliver the final exploratory data analysis research plan using R Markdown. When you are finished with this course, you will have the skills and knowledge needed to develop an exploratory data analysis research plan that may be implemented in your next data science project. Software required: R programming and R Studio.

Table of contents
  1. Course Overview
  2. The Purpose of an Exploratory Data Analysis Research Plan
  3. Beginning Our Data Exploration
  4. Data Modeling
  5. Delivering the Analysis to the Final Audience
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