Introduction to Data Science, Machine Learning & AI using R

Introduction to Data Science Course Outline

• 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 Data

  Mining unstructured data for business applications

  • Preprocessing unstructured data in preparation for deeper analysis
  • Describing a corpus of documents with a term–document matrix
  • Make predictions from textual data

• Predicting Outcomes with Regression Techniques

  Estimating future values with linear regression

  • Modelling the numeric relationship between an output variable and several input variables
  • Correctly interpreting coefficients of continuous data
  • Assess your regression models for ‘goodness of fit’

• Categorising Data with Classification Techniques

  Automating the labelling of new data items

  • Predicting target values using Decision Trees
  • Constructing training and test data sets for predictive model building
  • Dealing with issues of overfitting

  Assessing model performance

  • Evaluating classifiers with confusion matrices
  • Calculating a model’s error rate

• Detecting Patterns in Complex Data with Clustering and Social Network 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

  Constructing recommendation engines

  • Cross–selling, up–selling and substitution as motivations
  • Leveraging recommendations based on collaborative filtering

• Learning from Data Examples with Neural Networks

  Machine learning with neural networks

  • Learning the weight of a neuron
  • Learning about how neural networks are being applied to object recognition, image segmentation, human motion and language modelling
  • Analysing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition

• Implementing Analytics within Your Organisation

  Expanding analytic capabilities

  • Breaking down Data Analytics into manageable steps
  • Integrating analytics into current business processes
  • Reviewing Hadoop, Spark, and Azure services for machine learning

  Dissemination and Data Science policies

  • Examining ethical questions of privacy in Data Science
  • Disseminating results to different types of stakeholders
  • Visualising data to tell a story