Instructional Video17:39
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 2

Higher Ed
In this video, we will cover K-means clustering from scratch- part 2. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
Instructional Video18:55
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Statistical Based Methods

Higher Ed
In this video, we will cover statistical based methods. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects)...
Instructional Video11:42
Instructional Video11:09
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Pandas for Plotting: Pandas for Plotting

Higher Ed
In this video, we will cover Pandas for plotting. This clip is from the chapter "Basics for Data Science: Data Understanding and Data Visualization with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Instructional Video9:27
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Pandas for Data Manipulation and Understanding: Pandas Rolling

Higher Ed
In this video, we will cover Pandas Rolling. This clip is from the chapter "Basics for Data Science: Data Understanding and Data Visualization with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Instructional Video11:04
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Seaborn for Data Visualization: Introduction to Seaborn

Higher Ed
In this video, we will cover an introduction to Seaborn. This clip is from the chapter "Basics for Data Science: Data Understanding and Data Visualization with Python" of the series "Data Science and Machine Learning (Theory and...
Instructional Video7:18
Curated Video

Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Methods: Clustering Practice with Python

Higher Ed
In this video, we will cover clustering practice with Python. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section,...
Instructional Video5:43
Curated Video

Determining Normal Distribution Using the Empirical Rule

K - 5th
In this video, the teacher explains how to determine if a distribution is normally distributed by applying the empirical rule. The video covers the conditions that must be checked. Examples are provided to illustrate the application of...
Instructional Video4:53
Brian McLogan

Learn how to create a box and whisker plot for a set of data

12th - Higher Ed
👉 Learn how to find the mean, the median, and the mode of a set of data. The (arithmetic) mean of a set of data is the average of the set of data and is obtained by adding up the numbers in the set of data and dividing it by the count of...
Instructional Video13:20
Curated Video

Predictive Analytics with TensorFlow 2.1: Using Statistics in Predictive Modeling

Higher Ed
In this video, we will discuss some widely used statistical concepts required in predictive analytics, followed by some basic understanding of predictive modeling, such as random sampling, central limit theorem, hypothesis testing using...
Instructional Video5:43
Curated Video

Solving Quadratic Equations using the Quadratic Formula

K - 5th
In this video, the teacher explains how to solve quadratic equations using the quadratic formula. They highlight common mistakes to avoid, such as squaring negative numbers and correctly identifying the coefficients.
Instructional Video8:01
Brian McLogan

How to find the variance and standard deviation from a set of data

12th - Higher Ed
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard...
Instructional Video2:33
Flipping Physics

Calculating the Uncertainty of the Coefficient of Friction

12th - Higher Ed
10 trials to calculate the coefficient of static friction and how to calculate the uncertainty of this measurement.
Instructional Video6:38
Curated Video

Statistics for Data Science and Business Analysis - Test for the Mean; Population Variance Known

Higher Ed
This video is about test for the mean when population variance is known. This clip is from the chapter "Hypothesis Testing" of the series "Statistics for Data Science and Business Analysis".This section explains null and alternative...
Instructional Video8:15
Brian McLogan

Learning how to find the variance and standard deviation from a set of data

12th - Higher Ed
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard...
Instructional Video13:47
Catalyst University

Independent t-Test and F-Test: Theory and Excel Calculation

Higher Ed
Independent t-Test and F-Test: Theory and Excel Calculation
Instructional Video7:49
APMonitor

Python Basic Statistical Analysis

10th - Higher Ed
Python statistical functions such as average, maximum, minimum, standard deviation, and custom counting are demonstrated in an iPython notebook.
Instructional Video4:32
Curated Video

Mean Absolute Deviation vs Standard Deviation

K - 5th
In this video, the teacher explains the difference between mean absolute deviation (MAD) and standard deviation, which are both measures of spread in a set of data. The teacher demonstrates how to calculate both MAD and standard...
Instructional Video3:34
Curated Video

Statistics for Data Science and Business Analysis - The Standard Normal Distribution

Higher Ed
This video explains the standard normal distribution by deriving it from the normal distribution through the method of standardization. You will also be elaborated on its use for testing. This clip is from the chapter "Inferential...
Instructional Video6:56
Curated Video

Understanding Z-Scores

K - 5th
In this video, the teacher explains the concept of z-scores and how they can be used to compare data values from different distributions. The teacher uses examples of SAT and ACT scores to demonstrate how z-scores can provide a...
Instructional Video3:57
Curated Video

Statistics for Data Science and Business Analysis - The Normal Distribution

Higher Ed
This video introduces the normal distribution and its great importance to statistics as a field. This clip is from the chapter "Inferential Statistics Fundamentals" of the series "Statistics for Data Science and Business Analysis".This...
Instructional Video6:46
Curated Video

Understanding and Interpreting the Margin of Error

K - 5th
In this video, the teacher explains how researchers use the margin of error to interpret data. They discuss the properties of the normal distribution and provide formulas to calculate the margin of error for different scenarios.
Instructional Video4:08
Curated Video

Analyzing Data: Comparing Sets with Outliers

K - 5th
In this video, the teacher explains how to compare two sets of data when there is an outlier. They discuss the use of measures of center and measures of spread, such as median and interquartile range, to analyze the data.