DATA SCIENCE

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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