Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them.
DurationOverview
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them.
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
Course Outline
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 Classification
Introduction To Data Analytics Lifecycle
Discovery
Data preparation
Model planning
Model building
Presentation/Communication of results
Operationalization
Exercise: Case study
From this point most of the training time (80%) will be spent on examples and exercises in R and related big data technology.
Getting Started With R
Installing R and Rstudio
Features of R language
Objects in R
Data in R
Data manipulation
Big data issues
Exercises
Getting Started With Hadoop
Installing Hadoop
Understanding Hadoop modes
HDFS
MapReduce architecture
Hadoop related projects overview
Writing programs in Hadoop MapReduce
Exercises
Integrating R And Hadoop With RHadoop
Components of RHadoop
Installing RHadoop and connecting with Hadoop
The architecture of RHadoop
Hadoop streaming with R
Data analytics problem solving with RHadoop
Exercises
Pre-Processing And Preparing Data
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
Exploratory Data Analytic Methods In R
Descriptive statistics
Exploratory data analysis
Visualization – preliminary steps
Visualizing single variable
Examining multiple variables
Statistical methods for evaluation
Hypothesis testing
Exercises and Case study
Data Visualizations
Basic visualizations in R
Packages for data visualization ggplot2, lattice, plotly, lattice
Formatting plots in R
Advanced graphs
Exercises
Regression (Estimating Future Values)
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
Classification
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
Assessing Model Performance And Selection
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
Ensemble Methods
Bagging
Random Forests
Boosting
Gradient boosting
Exercises and Case study
Support Vector Machines For Classification And Regression
Maximal Margin classifiers
Exercises and Case study
Identifying Unknown Groupings Within A Data Set
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
Discovering Connections With Link Analysis
Link analysis concepts
Metrics for analyzing networks
The Pagerank algorithm
Hyperlink-Induced Topic Search
Link Prediction
Exercises and Case study
Association Pattern Mining
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
Constructing Recommendation Engines
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
Text analysis steps
Collecting raw text
Bag of words
Term Frequency –Inverse Document Frequency
Determining Sentiments
Exercises and Case study
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