Symbolic data analysis (SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are made output as a result. The data units are called symbolic since they are more complex than standard ones, as they not only contain values or categories, but also include internal variation and structure. SDA is based on four spaces: the space of individuals, the space of concepts, the space of descriptions, and the space of symbolic objects. The space of descriptions models individuals, while the space of symbolic objects models concepts. (Wikipedia).
Statistical Analysis Spreadsheet (1 of 3: Calculating expected values)
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From playlist Probability and Discrete Probability Distributions
From playlist Exploratory Data Analysis
Data that are collected for statistical analysis can be classified according to their type. It is important to know what data type we are dealing with as this determines the type of statistical test to use.
From playlist Learning medical statistics with python and Jupyter notebooks
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Symbolic Algebra in Mathematica 6 with Abby Brown
http://www.wolfram.com/screencasts
From playlist Screencasts
Mioara Joldes: Validated symbolic-numerci algorithms and practical applications in aerospace
In various fields, ranging from aerospace engineering or robotics to computer-assisted mathematical proofs, fast and precise computations are essential. Validated (sometimes called rigorous as well) computing is a relatively recent field, developed in the last 20 years, which uses numerica
From playlist Probability and Statistics
Business Data Analysis with Excel
Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation wher
From playlist Data Analytics Tutorials
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In this lesson we will consider analysis of categorical data.
From playlist Learning medical statistics with python and Jupyter notebooks
In this lesson we take a look at comparing categorical data with tests such as the chi-square test.
From playlist Learning medical statistics with python and Jupyter notebooks
Compilation - Part Three: Syntax Analysis
This is part three of a series of videos about compilation. Part three is about syntax analysis. It explains how the syntax analyser, otherwise known as the parser, takes a token stream from the lexical analyser, and checks it to make sure that the rules of the source language have been
From playlist Compilation
Marcelo Frias: Relational tight field bounds for distributed analysis of programs
HYBRID EVENT Recorded during the meeting "19th International Conference on Relational and Algebraic Methods in Computer Science" the November 3, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other t
From playlist Virtual Conference
Cracking the Neural Code with Mathematica
Kai Gensel To learn more about the Wolfram Technologies, visit http://www.wolfram.com The European Wolfram Technology Conference featured both introductory and expert sessions on all major technologies and many applications made possible with Wolfram technology. Learn to achieve insight
From playlist European Wolfram Technology Conference 2015
Boris Beranger - Composite likelihood and logistic regression models for aggregated data
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From playlist Statistics Across Campuses
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From playlist Wolfram Technology Conference 2019
Lecture 11: The Importance of Data Representation
MIT HST.512 Genomic Medicine, Spring 2004 Instructor: Dr. Alvin Thong-Juak Kho View the complete course: https://ocw.mit.edu/courses/hst-512-genomic-medicine-spring-2004/ YouTube Playlist: https://www.youtube.com/watch?v=_-gQchCLmXk&list=PLUl4u3cNGP613PJMNmRjAIdBr76goU1V5 We'll go throug
From playlist MIT HST.512 Genomic Medicine, Spring 2004
Highline Excel 2016 Class 01: Excel Fundamentals: Efficiency, Data, Data Sets, Formatting
Download Files: https://people.highline.edu/mgirvin/AllClasses/218_2016/218Excel2016.htm In this video learn about these Excel Fundamentals for Excel Highline Excel 2016 Class: 1. (04:24) Download Files 2. (00:33) Prerequisites for class 3. (01:04) Excel Professional 2016 4. (01:50) Instal
From playlist Excel Advanced Free Course at YouTube. Comprehensive Excel 2016: Calculations & Data Analysis (27 Videos)
R & Python - Introduction to Human Language Modeling
Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class - this video set covers the updated version with both R and Python. This first video covers the introduction to the course including some basic back
From playlist Human Language (ANLY 540)
Python for Data Analysis: Basic Data Types
This video covers the basic data types built into Python including integers, floats, strings, booleans and None. This is lesson 3 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below: Python for Data Analy
From playlist Python for Data Analysis
Automated Vulnerability Detection in Source Code Using Deep Learning (algorithm) | AISC
Toronto Deep Learning Series, 3 December 2018 Paper: https://arxiv.org/abs/1807.04320 Speaker: Alex Hesammohseni & Angshuman Ghosh (Loblaw Digital) Host: Loblaw Digital Date: Dec 3rd, 2018 Automated Vulnerability Detection in Source Code Using Deep Representation Learning Increasing n
From playlist Natural Language Processing