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Introduction to Data Science, Machine Learning & AI

If you want to become a data scientist, this is the course to begin with. Using open source tools, it covers all the concepts necessary to move through the entire data science pipeline, and whether you intend to continue working with open source tools, or later opt for proprietary services, this course will give you the foundation you need to assess which options best suit your needs.

Key Features of this Introduction to Data Science Training:

  • 5 days of instructor-led training
  • After-course instructor coaching benefit
  • End-of-course exam included
  • After-course computing sandbox included

This Data Science, Machine Learning & AI training course includes 29 hours of Instructor-Led Training (ILT) or Virtual Instructor-Led Training (VILT) presented by a real-world data science expert. Attend in-person or online through our AnyWare virtual training platform.

Who Should Attend this Data Science Training

It’s designed for beginners, technical and non-technical.

Is this a good starting point for how to become a data scientist?

Yes, this course is designed as an introduction to data science, machine learning, and AI, and does not require any specialised or technical knowledge prior to attendance


Learning Objectives

You Will Learn How To:

  • Translate business questions into Machine Learning problems to understand what your data is telling you
  • Explore and analyse data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
  • Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviours as well as other business critical events
  • Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
  • Discover hidden customer behaviours from Association Rules and Build Recommendation Engines based on behavioral patterns
  • Use biologically-inspired Neural Networks to learn from observational data as humans do
  • Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis


There's no expectations regarding specific platforms except basic familiarity with a Windows environment.

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 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


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Virtual Classroom

Virtual classrooms provide all the benefits of attending a classroom course without the need to arrange travel and accomodation. Please note that virtual courses are attended in real-time, commencing on a specified date.

Virtual Course Dates

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