Data Science for Big Data Analytics Treningskurs

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
• 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
  • 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