Modeling

 

 


 

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E.g., 2017-08-22
E.g., 2017-08-22
E.g., 2017-08-22
Aug 28th 2017

Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars.

Average: 4 (1 vote)
Aug 28th 2017

Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars.

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Aug 28th 2017

This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.

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Aug 28th 2017

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Average: 6.1 (8 votes)
Aug 22nd 2017

This course gives you an introduction to modeling methods and simulation tools for a wide range of natural phenomena. The different methodologies that will be presented here can be applied to very wide range of topics such as fluid motion, stellar dynamics, population evolution, ...

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Aug 21st 2017

Learn a new way to approach problem solving by stating the problem and letting powerful constraint solving software do the rest. This class teaches you the art of encoding complex discrete optimization problems in the MiniZinc modeling language and then shows you how to effortlessly solve them by leveraging state-of-the-art open-source constraint solving software.

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Aug 21st 2017

Learn essential analytics models and methods and how to appropriately apply them, using tools such as R, to retrieve desired insights. Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.

Average: 5 (1 vote)
Aug 7th 2017

Welcome to Quantitative Formal Modeling and Worst-Case Performance Analysis. In this course, you will learn about modeling and solving performance problems in a fashion popular in theoretical computer science, and generally train your abstract thinking skills.

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Self-Paced

A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

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Self Paced

Aprenderemos a modelizar problemas del mundo real mediante su representación con grafos y a resolverlos mediante sus algoritmos asociados. Este curso trata la Teoría de Grafos desde el punto de vista de la modelización, lo que nos permitirá con posterioridad resolver muchos problemas de diversa índole. Presentaremos ejemplos de los distintos problemas en un contexto real, analizaremos la representación de éstos mediante grafos y veremos los algoritmos necesarios para resolverlos.

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May 30th 2017

Learn agile methods, object-oriented programing and best practices for analysis, design, testing and management in software engineering. The world of software engineering requires high flexibility, an influx of new ideas, and the courage to challenge traditional approaches. As a software engineer, you need to know the methods, workflows and tools to handle continuously growing complexity and shortened development cycles. You must be able to work in teams to build high-quality software.

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Self Paced

Learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Average: 8 (3 votes)