Visualization for exploring characteristics of data

Visualizing data distributions with boxplots, histograms and density plots

Identifying outliers in data

Unstructured data mining for business applications

Preprocessing unstructured data for deeper analysis

Using term–document matrix to describe a corpus of documents

Make predictions from textual data

Linear Regression Explained

Logistic Regression Explained

Modeling relationship output and input variables

Interpreting coefficients of continuous data

Assessing the ‘goodness of fit’ of Regression Models

Regression techniques for Big Data

Dealing with Data volume with RHadoop

Creating regression modules for RHadoop

Automated Labeling of new data items

Prediction using Decision Trees

Creating a Model from historical data for Predictions

Combining tree predictors with random forests in RHadoop

Assessing Model Performance

Visualizing Model Performance with a ROC curve

Confusion Matrices to evaluate Classifiers

Overview of Clustering and Link Analysis

Using K–Means Algorithm for Customer Market Segmentation

Defining Similarity using Appropriate Distance Measures

Creating Tree–like Clusters with Hierarchical Clustering

Clustering Text Documents and Tweets

Connections Discovery with Link Analysis

Capturing Connections with Social Network Analysis

Social Networks Results usage in Marketing

Building and evaluating association rules

Capturing true customer preferences in transaction data to enhance customer experience

Calculating support, confidence and lift to distinguish “good” rules from “bad” rules

Differentiating actionable, trivial and inexplicable rules

Meeting the challenge of large data sets when searching for rules with RHadoop

Constructing recommendation engines

Cross–selling, up–selling and substitution as motivations

Leveraging recommendations based on collaborative filtering

Expanding analytic capabilities

Breaking down Big Data Analytics into manageable steps

Integrating analytics into current business processes

Reviewing Spark, MLib and Mahout for machine learning

Examining ethical questions of privacy in Big Data

Disseminating results to different types of stakeholders