In statistics, data transformation is the application of a deterministic mathematical function to each point in a data set—that is, each data point zi is replaced with the transformed value yi = f(zi), where f is a function. Transforms are usually applied so that the data appear to more closely meet the assumptions of a statistical inference procedure that is to be applied, or to improve the interpretability or appearance of graphs. Nearly always, the function that is used to transform the data is invertible, and generally is continuous. The transformation is usually applied to a collection of comparable measurements. For example, if we are working with data on peoples' incomes in some currency unit, it would be common to transform each person's income value by the logarithm function. (Wikipedia).
Data science describes the activities related to collecting, storing and creating value from data. Creating value from data means using it to do useful things, like making better decisions. By analyzing data we can detect patterns in it and understand the process that generated it. This i
From playlist Data Science Dictionary
Data Transformation | Introduction to Data Mining part 16
In this Data Mining Fundamentals tutorial, we discuss the transformation of data in data preprocessing, such as attribute transformation. Attribute transformation is a function that maps the entire set of values of a given attribute to a new set of replacement values such that each old val
From playlist Introduction to Data Mining
Linear Transformations on Random Variables
I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: Discrete and continuous variables including
From playlist Older Statistics Videos and Other Math Videos
Statistics (video 1) - Statistics of Datasets
Recordings of the corresponding course on Coursera. If you are interested in exercises and/or a certificate, have a look here: https://www.coursera.org/learn/pca-machine-learning
From playlist Statistics of Datasets
Data transformation using the Wolfram Language
In this short video I show you how to transform data for use in parametric tests. When data does not meet the assumption of normality, we can transform it using non-linear functions. I state, though, that it is probably better to use distribution-independent (non-parametric) tests. In t
From playlist Statistics
Inverse Transform Sampling : Data Science Concepts
Let's take a look at how to transform one distribution into another in data science! Note: I should have included a lambda in front of the exponential PDF. I mistakenly forgot it. I appreciate the comments which helped me realize this mistake. --- Like, Subscribe, and Hit that Bell to g
From playlist Data Science Concepts
Statistics - The vocabulary of statistics
This video will give show you a few terms that are used in statistics such as data, population, sample, parameter, statistic, and variable. Remember that it matters if you are talking about the whole group, or a portion of that group. For more videos please visit http://www.mysecretmatht
From playlist Statistics
This video explains how to determine mean, median and mode. It also provided examples. http://mathispower4u.yolasite.com/
From playlist Statistics: Describing Data
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
Data Transformation Workflows with Anton Antonov, Session #1
In this first lecture, Anton introduces the target data transformation workflows and related concepts. Access the notebook for this lecture here: https://wolfr.am/DataTransformationsWorkflows1 You can interact directly with Anton through the Wolfram Community: https://wolfr.am/CommunityDa
From playlist Data Transformation Workflows with Anton Antonov
Signal nonstationarities and their effects on the power spectrum
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #2) Static spectral analysis
Standard Deviation and Linear Transformations
An introdution to Standard Deviation, it's properties, and the linear transformation process. LINEAR TRANSFORMATION AT 9:01 Check out http://www.ProfRobBob.com, there you will find my lessons organized by class/subject and then by topics within each class. Find free review test, useful
From playlist AP Statistics
Suhasini Subba Rao: Fourier based methods for spatial data observed on irregularly spaced locations
Abstract : In this talk we introduce a class of statistics for spatial data that is observed on an irregular set of locations. Our aim is to obtain a unified framework for inference and the statistics we consider include both parametric and nonparametric estimators of the spatial covarianc
From playlist Probability and Statistics
Neuroscience source separation 1a: Spectral separation
This is part one of a three-part lecture series I taught in a masters-level neuroscience course in fall of 2020 at the Donders Institute (the Netherlands). The lectures were all online in order to minimize the spread of the coronavirus. That's good for you, because now you can watch the en
From playlist Neuroscience source separation (3-part lecture series)
Why neural networks aren't neural networks
There is a better way to understand how AIs sort data, process images, and make decisions! Made for the 2021 Summer of Math Exposition: https://www.3blue1brown.com/blog/some1 Source code available here: https://gitlab.com/samsartor/nn_vis The background music is an excerpt of the endles
From playlist Summer of Math Exposition Youtube Videos
Data Science - Part IV - Regression Analysis and ANOVA Concepts
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-leve
From playlist Data Science
Geostatistics session 5 conditional simulation
Introduction to conditional simulation with Gaussian processes
From playlist Geostatistics GS240
Visu Makam: "Maximum Likelihood Estimation for Tensor Normal Models"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop IV: Efficient Tensor Representations for Learning and Computational Complexity "Maximum Likelihood Estimation for Tensor Normal Models" Visu Makam - Institute for Advanced Study Abstract: We study sa
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Broad overview of EEG data analysis analysis
This lecture is a very broad introduction to the most commonly used data analyses in cognitive electrophysiology. There is no math, no Matlab, and no data to download. For more information about MATLAB programming: https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10 For
From playlist OLD ANTS #1) Introductions
Statistics (video 5): Linear Transformations, Part 1/2
Recordings of the corresponding course on Coursera. If you are interested in exercises and/or a certificate, have a look here: https://www.coursera.org/learn/pca-machine-learning
From playlist Statistics of Datasets