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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 Mining Process and Problems
- Association Pattern Mining
- Data Clustering
- Outlier Detection
- Data Classification
Introduction to Data Analytics Lifecycle for Government
- 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 for Government
- Installing R and Rstudio
- Features of R Language
- Objects in R
- Data in R
- Data Manipulation
- Big Data Issues
- Exercises
Getting Started with Hadoop for Government
- Installing Hadoop
- Understanding Hadoop Modes
- HDFS
- MapReduce Architecture
- Hadoop Related Projects Overview
- Writing Programs in Hadoop MapReduce
- Exercises
Integrating R and Hadoop with RHadoop for Government
- 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 for Government
- 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 for Government
- 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 for Government
- Basic Visualizations in R
- Packages for Data Visualization: ggplot2, lattice, plotly, lattice
- Formatting Plots in R
- Advanced Graphs
- Exercises
Regression (Estimating Future Values) for Government
- 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 for Government
- 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 for Government
- 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 for Government
- Bagging
- Random Forests
- Boosting
- Gradient Boosting
- Exercises and Case Study
Support Vector Machines for Classification and Regression for Government
- 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 for Government
- 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 for Government
- Link Analysis Concepts
- Metrics for Analyzing Networks
- The PageRank Algorithm
- Hyperlink-Induced Topic Search
- Link Prediction
- Exercises and Case Study
Association Pattern Mining for Government
- 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 for Government
- 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 for Government
- Text Analysis Steps
- Collecting Raw Text
- Bag of Words
- Term Frequency – Inverse Document Frequency
- Determining Sentiments
- Exercises and Case Study
35 Hours
Testimonials (2)
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics
The example and training material were sufficient and made it easy to understand what you are doing.
