What is R? During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn. Watch this 90 second video for an introduction to R This video is free to download, remix and share!
Matlab Matlab is a tool for doing numerical computations with matrices and vectors. It can also display information graphically. The best way to learn what Matlab can do is to work through some examples at the computer. After reading the " getting started " section, you can use the tutorial below for this. Getting started Julia Studio Beginner These tutorials will help you to familiarize yourself with the Julia Studio environment and the basics of the language. Hello, World! 1 Star This tutorial will walk you through creating a project in Julia Studio and have your write your first program. Start Tutorial Prime Numbers 1 Star Learn how to define functions and get your hands dirty with some actual math. Start Tutorial Quadratic Equation Solver 2 Stars Julia gives you all the power of a functional programming language, with the performance you would expect from C++.
It's the effect size, stupid: what effect size is and why it is important It's the Effect Size, StupidWhat effect size is and why it is important Robert CoeSchool of Education, University of Durham, email email@example.com Paper presented at the Annual Conference of the British Educational Research Association, University of Exeter, England, 12-14 September 2002 Abstract ARIMA Modeling with R - Listen Data This tutorial explains the theoretical concepts of time series and ARIMA modeling and how we can forecast series using ARIMA with R. Time Series A time series is a data series consisting of several values over a time interval. e.g. daily Stock Exchange closing point, weekly sales and monthly profit of a company etc. Typically, in a time series it is assumed that value at any given point of time is a result of its historical values. This assumption is the basis of performing a time series analysis. ARIMA technique exploits the auto-correlation (Correlation of observation with its lags) for forecasting.
Federal Reserve Economic Data Skip to main content Economic Research Federal Reserve Bank of St. Louis HyperStat Online: An Introductory Statistics Textbook and Online Tutorial for Help in Statistics Courses Recommend HyperStat to your friends on Facebook Click here for more cartoons by Ben Shabad. Other Sources Stat Primer by Bud Gerstman of San Jose State University
Introduction to Analytics Modeling Please note that the verified certificate option for this course is limited to 300 learners. The verified certificate option will close when this limit is reached. 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. In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
Bloomberg Enterprise Solutions Seamless workflow depends on consistency. Front, middle, and back-office teams working from one version of reality. Never wasting a single second trying to reconcile two numbers that should match — but don’t. Getting the right data at a sensible price, with fewer sources, no duplicates and no gaps. That’s why you need information from Bloomberg Enterprise Solutions. Today’s imperatives are consolidation and cost-cutting on an enterprise scale. StatPrimer © B. Gerstman 2003 StatPrimer (Version 6.4) B. Burt Gerstman (email) Part A (Introductory) (1) Measurement and sampling [Exercises] (2) Frequency distributions [Exercises]
Trefethen numerical ODE/PDE textbook Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations Lloyd N. Trefethen Available online -- see below This 325-page textbook was written during 1985-1994 and used in graduate courses at MIT and Cornell on the numerical solution of partial differential equations. The book has not been completed, though half of it got expanded into Spectral Methods in MATLAB.