Describe a data distribution from a graph (spread direction, shape symmetric and gaps).

Describe a data distribution from a graph (spread direction, shape symmetric and gaps).

New ReviewThis brief introduction to distribution elaborates on what constitutes data distribution and the kinds of distribution you will learn about.

Describe a data distribution (number of observations, median, range, mode, maximum and minimum).

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...

In this video, the teacher explains the concept of shape in data distribution using different types of graphs. They discuss histograms, line plots, and bell curves, and how to interpret the shape of the data based on symmetry and...

New ReviewAfter understanding the data distribution concept, we will look at the first type, the normal distribution.

In this lesson, students will learn how to describe the distribution of data by using the mean absolute deviation. They will understand how to find the mean of a data set and calculate the deviation of each data point from the mean.

This video explains data distribution. This clip is from the chapter "Exploratory Data Analysis (EDA)" of the series "Practical Data Science Using Python".This section explains Exploratory Data Analysis.

In this video, the concept of range in statistics is explained. The range is a single number that represents the spread of data. It is found by subtracting the smallest number from the largest number in a data set. The video also...

In addition to summary statistics, data visualization helps to understand the data characteristics and how different variables are related.There are many examples of data visualization with Matplotlib, Seaborn, and Plotly. In this...

This video explains histograms and normal approximation. This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.

Summary statistics give valuable insights as one of the first steps in data engineering after the data is gathered. Statistics help to assess data quality and diversity. Data discovery with statistics is a common first activity and there...

This video explains marginal histograms and marginal boxplots. This clip is from the chapter "Matplotlib and Seaborn – Libraries and Techniques" of the series "Data Analytics Using Python Visualizations".This section introduces you to...

In this video, we will cover foundations, data types, and representing data. This clip is from the chapter "Diving in with Code" of the series "Probability / Statistics - The Foundations of Machine Learning".In this section, we will...

This video explains Seaborn Boxplot, Violin plot, and Categorical Scatterplot. This clip is from the chapter "Advanced Visualizations Using Business Applications" of the series "Data Analytics Using Python Visualizations".This section...

In this video, we will cover Seaborn Pairplot using Iris data. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and Projects) A to...

Once data is read into Python, a first step is to analyze the data with summary statistics. This is especially true if the data set is large. Summary statistics include the count, mean, standard deviation, maximum, minimum, and quartile...

Describe a data distribution (number of observations, median, range, mode, maximum and minimum).

Learn how to read and interpret dot plots to identify clusters, peaks, and gaps in data. The examples given demonstrate how these patterns can provide insights into test scores and student performance. By understanding these visual...

Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine performance...

This video explains distributions. This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.

In this video, we will cover probability models towards random variables. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory...

This video explains the distribution of one feature. This clip is from the chapter "Exploratory Data Analysis" of the series "Python for Data Analysis: Step-By-Step with Projects".This section explains exploratory data analysis.