Tag: modeling

University of Cincinnati – Master of Science in Business Analytics


From the University of Cincinnati: “The University of Cincinnati Master of Science in Business Analytics program provides you with expertise in descriptive, predictive and prescriptive analytics. That’s one of the many reasons why the University of Cincinnati MS in Business Analytics has been ranked among the Top 10 business analytics programs in the United States and why many of our graduates are working business analysts and data scientists at world-leading … Continue Reading

University College Cork – Master of Science in Data Science and Analytics


From UCC: “The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through development of a research project, to investigate … Continue Reading

Kennesaw State University – PhD in Analytics and Data Science


From Kennesaw State: “Kennesaw State University’s PhD in Analytics and Data Science is an advanced degree which has been developed to meet the market demand for Data Science. This degree with train individuals to translate large, structured and unstructured, complex datasets to information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, he program also … Continue Reading

Bayesian Modelling in Python


“Welcome to ‘Bayesian Modelling in Python’ – a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn’t aim to be a bayesian statistics tutorial – but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below. Contents Introduction Motivation … Continue Reading

Bayesian machine learning

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From FastML: “So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together – we know it took us a while. This article is an introduction we wish we had back then.” This article covers the following topics: Bayesians and Frequentists Priors, updates, and posteriors Inferring model parameters from data Model vs inference Statistical modelling … Continue Reading

What’s Wrong With My Time Series


From MultiThreaded: “What’s wrong with my time series? Model validation without a hold-out set Time series modeling sits at the core of critical business operations such as supply and demand forecasting and quick-response algorithms like fraud and anomaly detection. Small errors can be costly, so it’s important to know what to expect of different error sources. The trouble is that the usual approach of cross-validation doesn’t work for time series … Continue Reading

Probability and Random Processes


From Amazon: “This book gives an introduction to probability and its many practical applications by providing a thorough, entertaining account of basic probability and important random processes, covering a range of important topics. Emphasis is on modelling rather than abstraction and there are new sections on sampling and Markov chain Monte Carlo, renewal-reward, queuing networks, stochastic calculus, and option pricing in the Black-Scholes model for financial markets. In addition, there … Continue Reading

Linear Regression and Modeling


This is course 3 of 5 in the Statistics with R Specialization. From Coursera: “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 … Continue Reading

DataCamp – Statistical Modeling in R (Part 2)


Part 1 of this course can be found here: Statistical Modeling in R Part 1. From DataCamp: “Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 2, we’ll take a look at effect size and interaction, the concepts of total and partial change, sampling variability and mathematical transforms, and the implications of something called … Continue Reading

DataCamp – Statistical Modeling in R (Part 1)


From DataCamp: “Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 1, we’ll take a look at what modeling is and what it’s used for, R tools for constructing models, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables. This course has … Continue Reading