<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="http://www.r-bloggers.com/data-mining-with-r-course-may-17-18/"></div></div><div><a data-url="http://www.r-bloggers.com/data-mining-with-r-course-may-17-18/" data-counturl="http://www.r-bloggers.com/data-mining-with-r-course-may-17-18/" data-count="horizontal" data-text="“Data Mining with R” Course | May 17-18" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://www.milanor.net/blog/data-mining-with-r-course-may-17-18/"> MilanoR</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>The<strong> two-days course</strong> <strong>Data Mining with R, </strong>is organized by the R training and consulting company Quantide<strong>. </strong><strong>Next live class</strong> is on<strong> May</strong><strong> 17-18 </strong>in<strong> Legnano (Milan).</strong></p>
<p>If you want to know more about Quantide, check out <a target="_blank" href="http://www.quantide.com/">Quantide’s website</a>.</p>
<p>If you wish to attend the class, reserve a seat on the <a target="_blank" href="http://www.eventbrite.com/e/r-live-class-data-mining-with-r-tickets-24782882268">course ticket page</a>.</p>
<p> </p>
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<h3>R Live Class – Data Mining with R</h3>
<h4>May 17-18, Legnano (Milan)</h4>
<p> </p>
<p>This course introduces some of most important and popular techniques in data-mining applications with R.<br>
Data mining is the computational process of discovering patterns in large data sets.<br>
During the two-days course we will review a wide variety of techniques to catch information from big amount of data: Dimensionality reduction, Clustering, Classification and Prediction examples will be presented and deepened.</p>
<h4><strong>Course organization</strong></h4>
<p><span data-sheets-value="[null,2,&quot;During the first day, an introduction to basic methods for data description is presented. After that, a review of most popular techniques for data/dimensionality reduction is shown; such techniques (Multidimensional Scaling, Principal Components Analysis, Correspondence Analysis) allow the analyst to &quot;extract&quot; the most relevant information from data, reducing the amount of analyzed variables. Next, a review of methods to search for &quot;natural subgroups&quot; (Hierachical/non hierarchical Cluster Analysis, Gaussian Mixtures Models) within data is shown.nThe end of first day and the begin of second day will be used to present techniques for the classification analysis, i.e., techniques used to explain or predict the membership of units to pre-defined classes (Linear/Quadratic Discriminant Analysis, Logistic Regression, K-Nearest-Neighborhood,...).nFinally, in remaining part of second day, a review of techniques for variables selection, collinearity reduction, and best prediction for regression models are shown (PCA regresssion, Ridge Regression, Lasso Regression, Elastic-Net regression, ..)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">The course will start with an introduction to basic methods for data description. After that, we will review the most popular techniques for data/dimensionality reduction, as Multidimensional Scaling, Principal Components Analysis, Correspondence Analysis. Next, we will focus on methods for searching for “natural subgroups” within data, as Hierachical/non hierarchical Cluster Analysis, Gaussian Mixtures Models.</span></p>
<p><span data-sheets-value="[null,2,&quot;During the first day, an introduction to basic methods for data description is presented. After that, a review of most popular techniques for data/dimensionality reduction is shown; such techniques (Multidimensional Scaling, Principal Components Analysis, Correspondence Analysis) allow the analyst to &quot;extract&quot; the most relevant information from data, reducing the amount of analyzed variables. Next, a review of methods to search for &quot;natural subgroups&quot; (Hierachical/non hierarchical Cluster Analysis, Gaussian Mixtures Models) within data is shown.nThe end of first day and the begin of second day will be used to present techniques for the classification analysis, i.e., techniques used to explain or predict the membership of units to pre-defined classes (Linear/Quadratic Discriminant Analysis, Logistic Regression, K-Nearest-Neighborhood,...).nFinally, in remaining part of second day, a review of techniques for variables selection, collinearity reduction, and best prediction for regression models are shown (PCA regresssion, Ridge Regression, Lasso Regression, Elastic-Net regression, ..)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">The end of first day and the begin of second day will present techniques for classification analysis (Linear/Quadratic Discriminant Analysis, Logistic Regression, K-Nearest-Neighborhood,…).<br>
</span></p>
<p><span data-sheets-value="[null,2,&quot;During the first day, an introduction to basic methods for data description is presented. After that, a review of most popular techniques for data/dimensionality reduction is shown; such techniques (Multidimensional Scaling, Principal Components Analysis, Correspondence Analysis) allow the analyst to &quot;extract&quot; the most relevant information from data, reducing the amount of analyzed variables. Next, a review of methods to search for &quot;natural subgroups&quot; (Hierachical/non hierarchical Cluster Analysis, Gaussian Mixtures Models) within data is shown.nThe end of first day and the begin of second day will be used to present techniques for the classification analysis, i.e., techniques used to explain or predict the membership of units to pre-defined classes (Linear/Quadratic Discriminant Analysis, Logistic Regression, K-Nearest-Neighborhood,...).nFinally, in remaining part of second day, a review of techniques for variables selection, collinearity reduction, and best prediction for regression models are shown (PCA regresssion, Ridge Regression, Lasso Regression, Elastic-Net regression, ..)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Finally, in remaining part of second day, we will review some techniques for variables selection, collinearity reduction, and best prediction for regression models (PCA regresssion, Ridge Regression, Lasso Regression, Elastic-Net regression, ..)</span></p>
<h4><strong>Price </strong></h4>
<p>Euro 800 + VAT</p>
<h4>Outline</h4>
<ul>
<li><span data-sheets-value="[null,2,&quot;- Univariate Descriptive Statisticsn- Reduction of Data Dimensions (MDS, PCA and EFA, CA)n- Clustering (HC, NHC, GMM)n- Classification (LDA, CLASS, KNN)n- Prediction (Several techniques to model data)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Univariate Descriptive Statistics</span></li>
<li><span data-sheets-value="[null,2,&quot;- Univariate Descriptive Statisticsn- Reduction of Data Dimensions (MDS, PCA and EFA, CA)n- Clustering (HC, NHC, GMM)n- Classification (LDA, CLASS, KNN)n- Prediction (Several techniques to model data)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Reduction of Data Dimensions (MDS, PCA and EFA, CA)</span></li>
<li><span data-sheets-value="[null,2,&quot;- Univariate Descriptive Statisticsn- Reduction of Data Dimensions (MDS, PCA and EFA, CA)n- Clustering (HC, NHC, GMM)n- Classification (LDA, CLASS, KNN)n- Prediction (Several techniques to model data)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Clustering (HC, NHC, GMM)</span></li>
<li><span data-sheets-value="[null,2,&quot;- Univariate Descriptive Statisticsn- Reduction of Data Dimensions (MDS, PCA and EFA, CA)n- Clustering (HC, NHC, GMM)n- Classification (LDA, CLASS, KNN)n- Prediction (Several techniques to model data)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Classification (LDA, CLASS, KNN) </span></li>
<li><span data-sheets-value="[null,2,&quot;- Univariate Descriptive Statisticsn- Reduction of Data Dimensions (MDS, PCA and EFA, CA)n- Clustering (HC, NHC, GMM)n- Classification (LDA, CLASS, KNN)n- Prediction (Several techniques to model data)&quot;]" data-sheets-userformat="[null,null,961,[null,0],null,null,null,null,null,0,1,4,0]">Prediction (Several techniques to model data)</span></li>
</ul>
<h2><strong>FAQ</strong></h2>
<h4><strong>Should I take this course?</strong></h4>
<p>This class will be a good fit for you if you are already using R and wants to get an overview of data-mining techniques with R. Some background in theoretical statistics, probability, linear and logistic regression is required.</p>
<h4><strong>What does the cost include?</strong></h4>
<p>The cost includes lunch, comprehensive course materials + 1 hour of individual online post course support for each student within 30 days from course date.</p>
<h4><strong>There is a students discount?</strong></h4>
<p>We offer an academic discount for those engaged in full time studies or research. Please contact us for further information at training[at]quantide[dot]com</p>
<h4><strong>What should I bring?</strong></h4>
<p>A laptop with the latest version of R and R-Studio.</p>
<h4><strong>Who will I learn from?</strong></h4>
<p>Enrico Pegoraro works in R training and consulting activities, with a special focus on Six Sigma, industrial statistical analysis and corporate training courses. Enrico graduated in Statistics from the University of Padua.<br>
He has taught statistical models and R for hundreds of hours during specialized and applied courses, in universities, masters and companies.</p>
<h4><strong>What language is the course taught?</strong></h4>
<p>This course is taught in italian. Course material in English language</p>
<h4><strong>How can I reach your place?</strong></h4>
<p>Legnano is about 30 min by train from Milano. Trains from Milano to Legnano are scheduled every 30 minutes, and Quantide premises are 3  walking minutes from Legnano train station.</p>
<h4><strong>How can I contact you if I have further questions?</strong></h4>
<p>You can contact us attraining[at]quantide[dot]com</p>
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<p>The post <a target="_blank" rel="nofollow" href="http://www.milanor.net/blog/data-mining-with-r-course-may-17-18/">“Data Mining with R” Course | May 17-18</a> appeared first on <a target="_blank" rel="nofollow" href="http://www.milanor.net/blog">MilanoR</a>.</p>
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