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Tom Sawyer Software always falls in love with the business problem before employing its deep problem solving skills and unparalleled mastery of the data/knowledge visualization and graph analysis domains. The result is innovative yet practical solutions that consistently exceed expectations.
Tom Sawyer Perspectives is a low-code, data-oriented graph visualization and analysis development platform. Integrated design and preview interfaces and extensive API libraries allow designers and developers to quickly create custom applications that intuitively solve big data problems.
There are many uses of graph network analysis, such as analyzing relationships in social networks, cyber threat detection, and identifying the people most likely to buy a product based upon shared preferences.
Graphical analysis of joining velocity, welding power, and force has emerged as a highly effective tool to fix a range of problems, including inconsistent welds, poor weld strength, and non-hermetic seals. It also helps detect a number of equipment-related maintenance issues involving the acoustic stack, power supply, and pneumatic system.
Our experts from the Technical Solutions Team are ready to provide support on all issues concerning the analysis of data and the use of Volume Graphics software to enable you to achieve optimum results.
Released May 2021, the sixth and final report in the EFS series presents a power system operational analysis of high electrification scenarios. The analysis includes detailed grid simulations of future power systems and electricity demand in the year 2050 developed in earlier EFS reports, particularly the demand- and supply-side scenarios described in the second and fourth reports.
This report also presents an analysis of the potential role and value of flexible load, using assumptions of demand-side flexibility described in Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification.
Released December 2017, the first report in the EFS series provides estimated cost and performance data for electric technologies considered in the study. The study applies a literature- and expert opinion-based approach in developing future projections of technology advancement to be used in the EFS scenario analysis. The data can also inform other researchers and analysts exploring electrification.
You can also read a graphics analysis of GTA5 by Adrian Courrèges here.Since both RDR2 and GTA5 are from the same company and uses the same engine, some of the techniques from GTA5 present here as well.
STMATH 341 Introduction to Statistical Inference (5) RSN
Stochastic concepts including probabilistic underpinnings of statistics, measures of central tendency, variability, correlation, distributions, sampling, and simulation. Exploratory data analysis including experiments, surveys, measures of association and inferential statistics. Credit is not given for both STMATH 341 and STMATH 390. Prerequisite: minimum grade of 2.0 in STMATH 124, B MATH 144, or MATH 124.
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STMATH 390 Probability and Statistics in Engineering (5) NSc
Covers concepts of probability and statistics; conditional probability, independence, random variable, and distribution functions; descriptive statistics, transformations, sampling errors, confidence intervals, least squares, and maximum likelihood; and exploratory data analysis and interactive computing. Credit is not given for both STMATH 341 and STMATH 390. Prerequisite: a minimum grade of 2.0 in either STMATH 224 or MATH 224.
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STMATH 392 Probability (5)
Introduction to probability theory including combinatorial analysis, conditional probability, independence, and random variables. Conditional expectation including its use in prediction, moment-generating functions, and the multivariate normal distribution. Theoretical results in probability theory such as the strong law of large numbers and the central limit theorem. Prerequisite: a minimum grade of 2.0 in STMATH 324; recommended: B BUS 220 if enrolled in the Actuarial Science Minor. Offered: W.
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STMATH 407 Linear Programming (5)
Maximize and minimize linear functions subject to constraints consisting of linear equations and inequalities. Define linear optimization models from problem description. Solve linear programming problems using the simplex method. Conduct duality and sensitivity analysis for linear programming. Prerequisite: a minimum grade of 2.0 in either STMATH 208 or MATH 208; a minimum grade of 2.0 in either STMATH 126 or MATH 126; and a minimum grade of 2.0 in either CSS 132, CSS 142, CSE 122, CSE 142, or AMATH 301.
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STMATH 424 Real Analysis I (5)
Introduction to real analysis: the real number system, metric spaces, the topology of real Euclidean space, the Heine-Borel Theorem, sequences, Cauchy sequences, series and tests for convergence, continuous functions, the intermediate and extreme value theorems, differentiability, the mean value theorem, power series, and Taylor's Theorem. Prerequisite: minimum grade of 2.0 in STMATH 300 or MATH 300.
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STMATH 467 Fostering Statistical Thinking, Data, and Graphical Analysis (5) NSc, RSN
Focuses on methods of teaching data and graphical analysis and statistical thinking from a developmental perspective, including how to foster secondary students' statistical thinking, and using technological tools to teach key concepts in secondary mathematics using big data sets, graphical analysis, and dynamic visualization. Prerequisite: minimum grade of 2.0 in STMATH 125 or Math 125. Offered: jointly with B EDUC 467.
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Graph-theoretic methods, in various forms, have proven particularly useful in linguistics, since natural language often lends itself well to discrete structure. Traditionally, syntax and compositional semantics follow tree-based structures, whose expressive power lies in the principle of compositionality, modeled in a hierarchical graph. More contemporary approaches such as head-driven phrase structure grammar model the syntax of natural language using typed feature structures, which are directed acyclic graphs. Within lexical semantics, especially as applied to computers, modeling word meaning is easier when a given word is understood in terms of related words; semantic networks are therefore important in computational linguistics. Still, other methods in phonology (e.g. optimality theory, which uses lattice graphs) and morphology (e.g. finite-state morphology, using finite-state transducers) are common in the analysis of language as a graph. Indeed, the usefulness of this area of mathematics to linguistics has borne organizations such as TextGraphs, as well as various 'Net' projects, such as WordNet, VerbNet, and others.
Graph theory is also widely used in sociology as a way, for example, to measure actors' prestige or to explore rumor spreading, notably through the use of social network analysis software. Under the umbrella of social networks are many different types of graphs.[17] Acquaintanceship and friendship graphs describe whether people know each other. Influence graphs model whether certain people can influence the behavior of others. Finally, collaboration graphs model whether two people work together in a particular way, such as acting in a movie together.
Graphs are also commonly used in molecular biology and genomics to model and analyse datasets with complex relationships. For example, graph-based methods are often used to 'cluster' cells together into cell-types in single-cell transcriptome analysis. Another use is to model genes or proteins in a pathway and study the relationships between them, such as metabolic pathways and gene regulatory networks.[18] Evolutionary trees, ecological networks, and hierarchical clustering of gene expression patterns are also represented as graph structures.
The paper written by Leonhard Euler on the Seven Bridges of Königsberg and published in 1736 is regarded as the first paper in the history of graph theory.[20] This paper, as well as the one written by Vandermonde on the knight problem, carried on with the analysis situs initiated by Leibniz. Euler's formula relating the number of edges, vertices, and faces of a convex polyhedron was studied and generalized by Cauchy[21] and L'Huilier,[22] and represents the beginning of the branch of mathematics known as topology.
Use a Python environment to discover insights and quickly demonstrate value from graph analysis. Data science teams can start experimenting quickly and get more projects completed with support from pre-configured graph algorithms and automated procedures.
Answer questions with graph-based queries, search, and pathfinding. Further your analysis and inference through a broad set of graph algorithms from centrality to node embedding and conduct graph-native unsupervised and supervised ML for clustering, similarity, classification, and more.
The majority of organizations in this country are faced with the need to drive improvements in productivity, quality and customer satisfaction in order to remain competitive. Increasingly, these organizations are choosing Lean Six Sigma as the way to achieve such goals. In this intensive 5-day course, learn how to contribute to and lead Lean Six Sigma improvement teams. Gain a strong knowledge of how to apply the Lean Six Sigma DMAIC methodology with a primary focus on process mapping, lean tools and methods as well as graphical analysis tools.
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