Easy Statistics: Data Visualization

Learn many different plots and graph techniques

Make sure to check out my twitter feed for promo codes and other updates (easystats3)

What you’ll learn

  • Data visualisation.
  • Basic plot types.
  • Intermediate plot types.
  • Advanced plot types.
  • Distribution plots.
  • Relationship plots.
  • Categorical plots.
  • Specialised plots.

Course Content

  • Introduction –> 1 lecture • 4min.
  • Single continuous variables –> 18 lectures • 1hr 39min.
  • Single discrete variables –> 5 lectures • 36min.
  • Relationship plots continuous –> 11 lectures • 1hr 7min.
  • Relationship plots discrete –> 5 lectures • 19min.
  • Relationships with three or more variables –> 1 lecture • 11min.

Easy Statistics: Data Visualization

Requirements

  • None.

Make sure to check out my twitter feed for promo codes and other updates (easystats3)

Learning and applying new statistical techniques can often be a daunting experience.

“Easy Statistics” is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.

This course will teach you the many different methods to visualize data

Visualizing and graphing data is a vital in modern data analytics. Whether you are a data scientist, student of quantitative methods or a business user, having an understanding of how to visualise data is an important aspect in getting data information across to other stakeholders. Many different ways of visualising data have been devised and some are better than other. However, each method has advantages and disadvantages and having a solid understanding of what visualization might be best suited is key to delivering a concise and sharp “data message”.

Often, it takes years of experience to accumulate knowledge of the different graphs and plots. In these videos, I will outline some of the most important data visualization methods and explain, without any equations or complex statistics, what are the advantages and disadvantages of each technique.

The main learning outcomes are:

  1. To learn and understand the basic methods of data visualization
  2. To learn, in an easy manner, variations and customisations of basic visualization methods
  3. To gain experience of different data visualization techniques and how to apply them

Themes include:

  • Histograms
  • Density plots
  • Spike plots
  • Rootograms
  • Box plots
  • Violin plots
  • Stem-and-Leaf plots
  • Quantile plots
  • Bar graphs
  • Pie charts
  • Dot charts
  • Radar plots
  • Scatter plots
  • Heat plots
  • Hex plots
  • Sunflower plots
  • Lines of best fit
  • Area plots
  • Line plots
  • Range plots
  • Rainbow plots
  • Jitter plots
  • Table plots
  • Baloon plots
  • Mosaic plots
  • and more