Data Science for Big Data Analytics
Corso
Online
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Descrizione
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Tipologia
Corso
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Metodologia
Online
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Inizio
Scegli data
I big data sono insiemi di dati così voluminosi e complessi che i tradizionali software applicativi per l'elaborazione dei dati sono inadeguati per gestirli Le sfide relative ai big data includono l'acquisizione di dati, l'archiviazione dei dati, l'analisi dei dati, la ricerca, la condivisione, il trasferimento, la visualizzazione, l'interrogazione, l'aggiornamento e la riservatezza delle informazioni .
Machine Translated
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Profilo del corso
I big data sono insiemi di dati così voluminosi e complessi che i tradizionali software applicativi per l'elaborazione dei dati sono inadeguati per gestirli Le sfide relative ai big data includono l'acquisizione di dati, l'archiviazione dei dati, l'analisi dei dati, la ricerca, la condivisione, il trasferimento, la visualizzazione, l'interrogazione, l'aggiornamento e la riservatezza delle informazioni .
Machine Translated
Opinioni
Materie
- Clustering
Programma
Introduction to Data Science for Big Data Analytics
- Data Science Overview
- Big Data Overview
- Data Structures
- Drivers and complexities of Big Data
- Big Data ecosystem and a new approach to analytics
- Key technologies in Big Data
- Data Mining process and problems
- Association Pattern Mining
- Data Clustering
- Outlier Detection
- Data Classification
- Discovery
- Data preparation
- Model planning
- Model building
- Presentation/Communication of results
- Operationalization
- Exercise: Case study
- Installing R and Rstudio
- Features of R language
- Objects in R
- Data in R
- Data manipulation
- Big data issues
- Exercises
- Installing Hadoop
- Understanding Hadoop modes
- HDFS
- MapReduce architecture
- Hadoop related projects overview
- Writing programs in Hadoop MapReduce
- Exercises
- Components of RHadoop
- Installing RHadoop and connecting with Hadoop
- The architecture of RHadoop
- Hadoop streaming with R
- Data analytics problem solving with RHadoop
- Exercises
- Data preparation steps
- Feature extraction
- Data cleaning
- Data integration and transformation
- Data reduction – sampling, feature subset selection,
- Dimensionality reduction
- Discretization and binning
- Exercises and Case study
- Descriptive statistics
- Exploratory data analysis
- Visualization – preliminary steps
- Visualizing single variable
- Examining multiple variables
- Statistical methods for evaluation
- Hypothesis testing
- Exercises and Case study
- Basic visualizations in R
- Packages for data visualization ggplot2, lattice, plotly, lattice
- Formatting plots in R
- Advanced graphs
- Exercises
- Linear regression
- Use cases
- Model description
- Diagnostics
- Problems with linear regression
- Shrinkage methods, ridge regression, the lasso
- Generalizations and nonlinearity
- Regression splines
- Local polynomial regression
- Generalized additive models
- Regression with RHadoop
- Exercises and Case study
- The classification related problems
- Bayesian refresher
- Naïve Bayes
- Logistic regression
- K-nearest neighbors
- Decision trees algorithm
- Neural networks
- Support vector machines
- Diagnostics of classifiers
- Comparison of classification methods
- Scalable classification algorithms
- Exercises and Case study
- Bias, Variance and model complexity
- Accuracy vs Interpretability
- Evaluating classifiers
- Measures of model/algorithm performance
- Hold-out method of validation
- Cross-validation
- Tuning machine learning algorithms with caret package
- Visualizing model performance with Profit ROC and Lift curves
- Bagging
- Random Forests
- Boosting
- Gradient boosting
- Exercises and Case study
- Maximal Margin classifiers
- Support vector classifiers
- Support vector machines
- SVM’s for classification problems
- SVM’s for regression problems
- Exercises and Case study
- Feature Selection for Clustering
- Representative based algorithms: k-means, k-medoids
- Hierarchical algorithms: agglomerative and divisive methods
- Probabilistic base algorithms: EM
- Density based algorithms: DBSCAN, DENCLUE
- Cluster validation
- Advanced clustering concepts
- Clustering with RHadoop
- Exercises and Case study
- Link analysis concepts
- Metrics for analyzing networks
- The Pagerank algorithm
- Hyperlink-Induced Topic Search
- Link Prediction
- Exercises and Case study
- Frequent Pattern Mining Model
- Scalability issues in frequent pattern mining
- Brute Force algorithms
- Apriori algorithm
- The FP growth approach
- Evaluation of Candidate Rules
- Applications of Association Rules
- Validation and Testing
- Diagnostics
- Association rules with R and Hadoop
- Exercises and Case study
- Understanding recommender systems
- Data mining techniques used in recommender systems
- Recommender systems with recommenderlab package
- Evaluating the recommender systems
- Recommendations with RHadoop
- Exercise: Building recommendation engine
- Text analysis steps
- Collecting raw text
- Bag of words
- Term Frequency –Inverse Document Frequency
- Determining Sentiments
- Exercises and Case study
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Data Science for Big Data Analytics