When collecting data to make observations about the world it usually just isn't possible to collect ALL THE DATA. So instead of asking every single person about student loan debt for instance we take a sample of the population, and then...

Today we’re going to finish up our unit on data visualization by taking a closer look at how dot plots, box plots, and stem and leaf plots represent data. We’ll also talk about the rules we can use to identify outliers and apply our new...

In this shorts video we will be presented with a standardized math test question and asked to find the median on a box and whisker plot. We will review the given problem. We will understand that inside the box of the box and whisker...

In this shorts video we will answer a standardized math test question by identifying IQR & Median on a Box-and-Whisker Plot. We will review each of the given box plots and identify the IQR as the width of the box. We will review each...

A video entitled "Representing with Box Plots" that covers the process of constructing a box plot by hand.

Pupil outcome: I can construct histograms and box plots utilising technology. Key learning points: - Histograms can be drawn quickly using technology - Using the raw data allows software to calculate equal bin widths easily - Box plots...

Pupil outcome: I can construct a box plot. Key learning points: - A box plot requires 5 pieces of information - These are lowest value, lower quartile, median, upper quartile and highest value - Outliers should be included in the range...

Pupil outcome: I can calculate the IQR and interpret what this means. Key learning points: - The interquartile range tells us how spread out the middle 50% of the data is - This can be seen visually with a box plot - Box plots and...

Pupil outcome: I can use my knowledge of cumulative frequency and histograms to solve problems. Key learning points: - Different representations of the same data set give a fuller picture - It is important to identify what features are...

Pupil outcome: I can interpret a box plot and compare two box plots. Key learning points: - A box plot allows us to make conclusions about the data set - Box plots can be compared to highlight similarities/differences in data sets - The...

Let's learn about a chart that is instantly recognizable to data scientists and statisticians: the distribution chart.

In this video, you will learn how to plot with Pandas.<br<br/>/>

This clip is from the chapter "Pandas" of the series "Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python".In this section, we will talk about Pandas.

Create a Box Plot creates a box plot by using given data.

In this video, the teacher explains how to find the difference in the range of data for two different stores using a double box plot. They define range as the difference between the minimum and maximum values in a...

If you're using R programming for data visualization then you're going to love this video. Creating plots and graphs using ggplot2 couldn't be easier. Ggplot using a concept called geoms (or geometries) to determine the type of plot that...

If you are doing data visualisation with ggplot the you'll find this video useful. ggplot2 is one of the tidyverse packages and is used to create graphs and plots. Data visualization is an important part of both understanding and...

This video explains building a box and whiskers plot with ggplot2.<br<br/>/>

This clip is from the chapter "Visualizing Data" of the series "R Programming for Statistics and Data Science".This section explains visualizing data.

Polymer properties such as density, melt index, and melt flow rate must be kept within tight specifications for each grade. This case study is to analyze polymer production data to predict melt flow...

In this video, the teacher explains how to compare two data sets using comparative box plots. They discuss the elements of a box plot, such as the minimum, maximum, median, lower quartile, upper quartile, and interquartile range. The...

Concrete mixtures have several variations. This data set is a case study for data visualization and exploration to predict the concrete compressive strength (MPa).



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This video gives an introduction to exploratory data analysis.
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This clip is from the chapter "Exploratory Data Analysis" of the series "Data Science and Machine Learning with R from A-Z Course [Updated for 2021]".This...

Much of data science and machine learning work is getting clean data into the correct form. This may include data cleansing to remove outliers or bad information, scaling for machine learning algorithms, splitting into...

If you want to use R programming to create plots and graphs using the ggplot package, then watch this video. In this video, I'll go though how to visualize two numeric variables and one categoric variable using line graphs and faceting...

Data visualization and exploration is one of the first steps in machine learning after the data is gathered and statistically summarized. It is used to graphically represent data to qualitatively understand relationships...