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Data Science Online Training Course Content

Introduction to R

Exploratory Data Analysis with R

• Loading, querying and manipulating data in R
• Cleaning raw data for modelling
• Reducing dimensions with Principal Component Analysis
• Extending R with user-defined packages

Facilitating good analytical thinking with data visualisation

• Investigating characteristics of a data set through visualisation
• Charting data distributions with boxplots, histograms and density plots
• Identifying outliers in data

Working with Unstructured and Large Data Sets

Mining unstructured data for business applications

• Preprocessing unstructured data in preparation for deeper analysis
• Describing a corpus of documents with a term-document matrix

Coping with the additional complexities of Big Data

• Examining the MapReduce and Hadoop architectures
• Integrating R and Hadoop with RHadoop

Predicting Outcomes with Regression Techniques

Estimating future values with linear and logistic regression

• Modelling the relationship between an output variable and several input variables
• Correctly interpreting coefficients of continuous and categorical data

Regression techniques for dealing with Big Data

• Overcoming issues of volume with RHadoop
• Creating regression modules for RHadoop

Categorising Data with Classification Techniques

Automating the labelling of new data items

• Predicting target values using Decision Trees
• Building a model from existing data for future predictions
• Combining tree predictors with random forests in RHadoop

Assessing model performance

• Visualising model performance with a ROC curve
• Evaluating classifiers with confusion matrices

Detecting Patterns in Complex Data with Clustering and Link Analysis

Identifying previously unknown groupings within a data set

• Segmenting the customer market with the K-Means algorithm
• Defining similarity with appropriate distance measures
• Constructing tree-like clusters with hierarchical clustering
• Clustering text documents and tweets to aid understanding

Discovering connections with Link Analysis

• Capturing important connections with Social Network Analysis
• Exploring how social networks results are used in marketing

Leveraging Transaction Data to Yield Recommendations and Association Rules

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

Implementing Analytics within Your Organisation

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

Dissemination and Big Data policies

• Examining ethical questions of privacy in Big Data
• Disseminating results to different types of stakeholders