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Parallel Computing Exercises: Snow and Rmpi (Part-3)

August 3, 2017
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Parallel Computing Exercises: Snow and Rmpi (Part-3)

The foreach statement, which was introduced in the previous set of exercises of this series, can work with various parallel backends. This set allows to train in working with backends provided by the snow and Rmpi packages (on a single machine with multiple CPUs). The name of the former package stands for “Simple Network of Related exercise sets: Parallel Computing...

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Parallel Computing Exercises: Foreach and DoParallel (Part-2)

July 13, 2017
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Parallel Computing Exercises: Foreach and DoParallel (Part-2)

In general, foreach is a statement for iterating over items in a collection without using any explicit counter. In R, it is also a way to run code in parallel, which may be more convenient and readable that the sfLapply function (considered in the previous set of exercises of this series) or other apply-alike functions. Related exercise sets: Parallel Computing...

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Parallel Computing Exercises: Snowfall (Part-1)

July 6, 2017
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Parallel Computing Exercises: Snowfall (Part-1)

R has a lot of tools to speed up computations making use of multiple CPU cores either on one computer, or on multiple machines. This series of exercises aims to introduce the basic techniques for implementing parallel computations using multiple CPU cores on one machine. The initial step in preparation for parallelizing computations is to Related exercise sets: Shiny Application...

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Density-Based Clustering Exercises

June 10, 2017
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Density-Based Clustering Exercises

Density-based clustering is a technique that allows to partition data into groups with similar characteristics (clusters) but does not require specifying the number of those groups in advance. In density-based clustering, clusters are defined as dense regions of data points separated by low-density regions. Density is measured by the number of data points within some Related exercise sets: Data science...

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Forecasting: ARIMAX Model Exercises (Part-5)

May 5, 2017
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Forecasting: ARIMAX Model Exercises (Part-5)

The standard ARIMA (autoregressive integrated moving average) model allows to make forecasts based only on the past values of the forecast variable. The model assumes that future values of a variable linearly depend on its past values, as well as on the values of past (stochastic) shocks. The ARIMAX model is an extended version of Related exercise sets:Forecasting: Linear...

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Forecasting: Multivariate Regression Exercises (Part-4)

May 1, 2017
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Forecasting: Multivariate Regression Exercises (Part-4)

In the previous exercises of this series, forecasts were based only on an analysis of the forecast variable. Another approach to forecasting is to use external variables, which serve as predictors. This set of exercises focuses on forecasting with the standard multivariate linear regression. Running regressions may appear straightforward but this method of forecasting is Related exercise sets:Forecasting: Linear...

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Forecasting: Exponential Smoothing Exercises (Part-3)

April 17, 2017
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Forecasting: Exponential Smoothing Exercises (Part-3)

Exponential smoothing is a method of finding patterns in time series, which can be used to make forecasts. In its simple form, exponential smoothing is a weighted moving average: each smoothed value is a weighted average of all past time series values (with weights decreasing exponentially from the most recent to the oldest values). In Related exercise sets:Forecasting: Time...

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Forecasting: Linear Trend and ARIMA Models Exercises (Part-2)

April 15, 2017
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Forecasting: Linear Trend and ARIMA Models Exercises (Part-2)

There are two main approaches to time series forecasting. One of them is to find persistent patterns in a time series itself, and extrapolate those patterns. Another approach is to discover how a series depend on other variables, which serve as predictors. This set of exercises focuses on the first approach, while the second one Related exercise sets:Multiple Regression...

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Forecasting: Time Series Exploration Exercises (Part-1)

April 10, 2017
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Forecasting: Time Series Exploration Exercises (Part-1)

R provides powerful tools for forecasting time series data such as sales volumes, population sizes, and earthquake frequencies. A number of those tools are also simple enough to be used without mastering sophisticated underlying theories. This set of exercises is the first in a series offering a possibility to practice in the use of such Related exercise sets:Stock Prices...

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Correlation and Correlogram Exercises

April 8, 2017
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Correlation and Correlogram Exercises

Correlation analysis is one of the most popular techniques for data exploration. This set of exercises is intended to help you to extend, speed up, and validate your correlation analysis. It allows to practice in: – calculating linear and nonlinear correlation coefficients, – testing those coefficients for statistical significance, – creating correlation matrices to study Related exercise sets:Accessing Dataframe...

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