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Course Outline
Introduction to Data Science for Big Data Analytics
- Overview of Data Science
- Overview of Big Data
- Data Structures
- Drivers and complexities of Big Data
- The Big Data ecosystem and a new approach to analytics
- Key technologies in Big Data
- Data Mining process and challenges
- Association Pattern Mining
- Data Clustering
- Outlier Detection
- Data Classification
Introduction to the Data Analytics Lifecycle
- Discovery
- Data preparation
- Model planning
- Model building
- Presentation and communication of results
- Operationalisation
- Exercise: Case study
From this point, approximately 80% of training time will be dedicated to practical examples and exercises using R and related big data technologies.
Getting Started with R
- Installing R and RStudio
- Key features of the R language
- Objects in R
- Working with data in R
- Data manipulation
- Big data challenges
- Exercises
Getting Started with Hadoop
- Installing Hadoop
- Understanding Hadoop operating modes
- HDFS (Hadoop Distributed File System)
- MapReduce architecture
- Overview of Hadoop-related projects
- Writing programs in Hadoop MapReduce
- Exercises
Integrating R and Hadoop with RHadoop
- Components of RHadoop
- Installing RHadoop and connecting to Hadoop
- The RHadoop architecture
- Hadoop streaming with R
- Solving data analytics problems with RHadoop
- Exercises
Pre-processing and Preparing Data
- Steps in data preparation
- Feature extraction
- Data cleaning
- Data integration and transformation
- Data reduction – sampling and feature subset selection
- Dimensionality reduction
- Discretisation and binning
- Exercises and case study
Exploratory Data Analysis Methods in R
- Descriptive statistics
- Exploratory data analysis
- Visualisation – preliminary steps
- Visualising single variables
- Examining multiple variables
- Statistical methods for evaluation
- Hypothesis testing
- Exercises and case study
Data Visualisations
- Basic visualisations in R
- Data visualisation packages: ggplot2, lattice, plotly
- 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 and the lasso
- Generalisations and nonlinearity
- Regression splines
- Local polynomial regression
- Generalised additive models
- Regression with RHadoop
- Exercises and case study
Classification
- Classification-related problems
- Bayesian refresher
- Naïve Bayes
- Logistic regression
- K-nearest neighbours
- 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 versus interpretability
- Evaluating classifiers
- Measures of model and algorithm performance
- Hold-out validation method
- Cross-validation
- Tuning machine learning algorithms with the caret package
- Visualising model performance using 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
- SVMs for classification problems
- SVMs for regression problems
- Exercises and case study
Identifying Unknown Groupings within a Dataset
- Feature selection for clustering
- Representative-based algorithms: k-means, k-medoids
- Hierarchical algorithms: agglomerative and divisive methods
- Probabilistic-based 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 analysing 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 using the recommenderlab package
- Evaluating recommender systems
- Recommendations with RHadoop
- Exercise: Building a recommendation engine
Text Analysis
- Steps in text analysis
- Collecting raw text
- Bag of words
- Term Frequency – Inverse Document Frequency (TF-IDF)
- 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.
