1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

• 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
  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
  Na?ve 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
  Support vector classifiers
  Support vector machines
  SVM’s for classification problems
  SVM’s for regression problems
  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