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    <title>Portfolios on ugofolio</title>
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    <description>Recent content in Portfolios on ugofolio</description>
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    <item>
      <title>WRAP-UP</title>
      <link>/portfolio/work80/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work80/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Tags</title>
      <link>/portfolio/work81/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work81/</guid>
      <description></description>
    </item>
    
    <item>
      <title>À la LaTeX</title>
      <link>/portfolio/work290/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work290/</guid>
      <description>&lt;p&gt;&lt;strong&gt;LATEST POST&lt;/strong&gt;&lt;br /&gt;
&lt;strong&gt;Topics:&lt;/strong&gt; quick reference and extensions to write in LaTeX.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Web-based Interactive Maps</title>
      <link>/portfolio/work280/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work280/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; responsive. True interactivity: zoom in, zoom out, markers, pop-ups, move around, etc.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>FUNNY 3!</title>
      <link>/portfolio/work275/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work275/</guid>
      <description>&lt;p&gt;&lt;/p&gt;</description>
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    <item>
      <title>Machine Learning; Classifiers &amp; Clusters</title>
      <link>/portfolio/work270/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work270/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; classification and clustering methods. Unsupervised techniques. Clustering: k-means, k-nearest neighbours, hierarchical clustering. Supervised techniques: regression, tree, random forests. Training, testing, predicting. Performance measures: Dunn&amp;rsquo;s index, ROC, AUC, confusion matrix.&lt;/p&gt;</description>
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    <item>
      <title>Survival of the Fittest</title>
      <link>/portfolio/work260/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work260/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; survival analysis. Event history analysis. Failure and churn analysis. Parametric, semiparametric, and nonparametric models: proportional hazards, accelerated failure time, exponential, piecewise exponential, Weibull, lognormal and Cox regression. Customer churn analysis. Censored and truncated data. Limited dependent variable and Tobit models.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Algorithms and Spam</title>
      <link>/portfolio/work250/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work250/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; analyze texts (emails) with algorithms. Differentiate spam and nonspam. Custom methods, tree-based methods, and Support Vector Machine. Train, test, and evaluate the methods.&lt;/p&gt;</description>
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    <item>
      <title>Sieving Data</title>
      <link>/portfolio/work240/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work240/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; data mining. Market basket analysis. Understanding consumer behaviour. Association rules or what is behind recommendation systems. data mining. Market basket analysis. Understanding consumer behaviour. Association rules or what is behind recommendation systems. Dimension reduction. Multidimensional scaling. Factorial analysis, Component analysis (principal, simple, multiple). Linear discriminant analysis. Feature selection.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Infographic software</title>
      <link>/portfolio/work230/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work230/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; experimenting with Tableau. Infographic examples.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>FUNNY 2!</title>
      <link>/portfolio/work225/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work225/</guid>
      <description>&lt;p&gt;&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Geospatial Analysis and Geostatistics</title>
      <link>/portfolio/work220/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work220/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt;  introduction to geospatial models. Visualization with maps. Analyze the Australian Football League audience. Spatial autocorrelation. Autoregressive, lag and error models. Spatial logit and probit models. More advanced models.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Map Mashup &amp; Geointelligence</title>
      <link>/portfolio/work210/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work210/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; data visualization and map mashups. Introduction to spatial analysis. How to add intelligence to maps.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Tweet, Tweet</title>
      <link>/portfolio/work200/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work200/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; web scraping (tweets) with an API. Natural Language Processing. Select topics and keywords to capture tweets. Get up-to-the-minute data and measure delays between tweet (tweeting speed). Text mining and word clouds. Compare two topics: assess popularity with the Poisson distribution. Analyze and manipulate text strings.&lt;/p&gt;</description>
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    <item>
      <title>Descriptive &amp; Inferential Statistics</title>
      <link>/portfolio/work190/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work190/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; basic to advanced statistical methods. Analyze census data (US state population). Infer the population with sampling and bootstrapping. Simulations and Monte Carlos.&lt;/p&gt;</description>
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    <item>
      <title>Optimizing the Coffee</title>
      <link>/portfolio/work180/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work180/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; mathematical optimization. The cooling effect of cream in the coffee. Extrapolation and interpolation.
&lt;/p&gt;</description>
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    <item>
      <title>THOUGHTFUL...</title>
      <link>/portfolio/work175/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work175/</guid>
      <description>&lt;p&gt;&lt;/p&gt;</description>
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    <item>
      <title>Pythonic Stuff</title>
      <link>/portfolio/work170/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work170/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; a series of projects. A website using a simple web framework. Documentation websites using static site generators. A command-line game and an application to be downloaded and installed.&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Interactive Visualization</title>
      <link>/portfolio/work160/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work160/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; interactive data visualization and graphics.&lt;/p&gt;</description>
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    <item>
      <title>Visualization</title>
      <link>/portfolio/work150/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work150/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; show graphics and maps instead of explanation or simple data tables. Static visualization. Bring opaque data into general understanding. Storytelling with numbers. Present surveys and polling data.&lt;/p&gt;</description>
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    <item>
      <title>...and counting</title>
      <link>/portfolio/work140/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work140/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; Model consumer demand (unit sold). Predict trends. Poisson and Negative Binomial distributions for counting discrete events.
&lt;/p&gt;</description>
    </item>
    
    <item>
      <title>Data Storytelling</title>
      <link>/portfolio/work130/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work130/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; present to a technical and a nontechnical audience. Storytelling. Bring arcane subjects into general use. Use econometrics techniques. Pose hypotheses, set goals, perform analyses and draw conclusions.
&lt;/p&gt;</description>
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    <item>
      <title>FUNNY!</title>
      <link>/portfolio/work125/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work125/</guid>
      <description>&lt;p&gt;&lt;/p&gt;</description>
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    <item>
      <title>Forage de texte</title>
      <link>/portfolio/work121/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work121/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Sujets:&lt;/strong&gt; Sujets: traitement du langage naturel. Construire un corpus de textes. Explorer les statistiques. Visualiser les mots, les fréquences, les mots communs, les mots différents, les bigrammes. Utiliser des nuages, des graphiques à barres et des dendrogrammes.
&lt;/p&gt;</description>
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    <item>
      <title>Mining Text</title>
      <link>/portfolio/work120/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work120/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; natural language processing, sentiment analysis, and topic modeling. Build a corpus of texts (documents or any tweet, email, comment, publication, status, etc.). Download data using APIs. Populate a database. Explore the statistics. Filter and extract regular expressions. Visualize words, frequencies, ngrams. Assess sentiment, draw conclusions, and provide advice.&lt;/p&gt;

&lt;p&gt;&lt;/p&gt;</description>
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    <item>
      <title>Exploring Pitch Data</title>
      <link>/portfolio/work110/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work110/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; multivariate analysis and visual exploration. Clean and format datasets. Pitching velocity, mix, patterns, location in the ball-strike zone. Change by month, by game, by inning. Ball-strike count, early- and late-game situations. Velocity, impact, and contact rate.
&lt;/p&gt;</description>
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    <item>
      <title>Titanic: Getting the Picture</title>
      <link>/portfolio/work100/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work100/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; decision trees, k-NN, and random forests. Storytelling and narrative. Data exploration: tables vs Venn diagrams vs visualization. Train and test sets. Confusion Matrix. Folds and cross validation. Pruning and avoiding overfitting.&lt;/p&gt;</description>
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    <item>
      <title>Modeling Credit Risk</title>
      <link>/portfolio/work90/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>/portfolio/work90/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Topics:&lt;/strong&gt; logit, probit, loglog and decision trees. Descriptive statistics. Train and test sets. Predictions. Confusion Matrix and ROC. Bank loan portfolio acceptance rate, bad rate, and risk tolerance.
&lt;/p&gt;</description>
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