Analyzing Large Data with R

Syllabus Details

• Unit I:(Large Data, Pre-processing and Visualization) Large data: basic properties of large data, source of large data, types of data, data cleaning, handling and processing, methodological challenges and problems, applications; pre-processing: reading, selecting data, filtering data, handling missing values; visualization: issues with large-scale data visualization, software, examples.
• Unit II:(Data Transformation and Exploration) Data transformation: smoothing, manipulating, power transformations, Min-Max normalization, Z-transformation, Box-Cox transformation, Lowess method; data exploration: data summarization, clustering, dimensionality reduction.
• Unit III:(Modeling) Linear models, multi-collinearity in large data, PCA-based regression, non-linear regression, categorical response modeling, classification and regression trees.
• Unit IV:(Other Big Data) Data integration, multi source data, multi-level data, linked data, meta data.

Course Learning Objective

1. Explain R data objects and how large data can be handled for analysis.
2. Enter and manipulate large data in R in a way that makes sense.
3. Use R to model large data.
4. Apply the principles of R to combine additional data resources in R.
5. Discuss the applications of R in different disciplines of knowledge and in different contexts.

Recommended Books

1. Larose, D. T. and Larose, C. D. Data Mining and Predictive Analytics. Wiley, 2015
2. Ryza, Sandy, Laserson, Uri, Owen, Sean and Wills, Josh. Advanced Analytics with Spark. O’Reilly, 2015
3. Prabhanjan, N. T., Suresh, R. and Manjunath, B. G. A Course in Statistics with R. Wiley, 2016
4. Walkowiak, Simon. Big Data Analytics with R. Packt, 2016