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Data Science

- Concepts and Practice

  • Format
  • Bog, paperback
  • Engelsk

Beskrivelse

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

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  • Vægt1090 g
  • coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Regression Distribution Fraud Classification Evaluation Recall Data preparation Self-organizing maps Modeling Confidence Decision Trees Statistics Application OPTIMIZATION Chi-square test Sensitivity Linear regression Model-based forecasting Dimension reduction K-means Clustering Random forests Bagging Clustering Recommendation Logistic regression Prior knowledge Specificity Information gain Accuracy Collaborative filtering Precision Descriptive Analytics Matrix Factorization Correlation Standard deviation K-nearest neighbors Artificial neural networks Ensemble Data Exploration Feature selection: Support Roadmap Rule induction DBSCAN Boosting Association Histogram PCA KDD Model evaluation Logit ARIMA Market basket analysis Outlier Cross-Sectional Data Mean Data-Science Stemming Anomaly Recommender Crisp FP-Growth apriori Backward Elimination Centroid attribute weighting data mining process data-driven forecasting Content-based recommender Density-based Cartesian space Distance-based Classification Performance Forward Selection density clustering Inverse document frequency Keyword clustering Latent Factors Exponential Smoothing Item profile Machine learning-based forecasting Kohonen networks Frequent Items Filter type function fitting Neighborhood-based methods RapidMiner RapidMiner GUI Scatter Chart Regression model validity Supervised model–based approach Stop word filtering Item-based collaborative filtering Local outlier factor LOF Naïve Bayesian Lift Charts ROC curve n-grams Ratings matrix
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