Our leading course has transformed the machine-learning (ML), artificial intelligence (AI) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it.

This course is an intuitive, hands-on introduction to AI, data science and machine learning. This is your artificial intelligence 101, data science 101 and machine learning 101 as there is significant overlap. The training focuses on central concepts and key skills, leaving you with a deep understanding of the foundations of AI and data science and even some of the more advanced tools used in the field.

The skills taught are transferable to all software platforms, and the course does not involve coding, or require any coding knowledge or experience. A tool with a graphical user interface is used so you can focus on learning the central skills and ideas.

Key skills taught include building, assessing, selecting and deploying predictive models, as well as employing some of the most commonly used methods in the field, including general linear models (GLMs), and advanced methods such as random forests. This also makes it your predictive analytics 101.

The course also covers key issues of data science practice in a work environment, and directs you to a range of further learning directions.

See Course Outline below for detailed day-by-day outline.

This course will provide a conceptual overview and practical hands-on experience of a wide range of key tools, techniques and processes.

At the heart of the data mining toolkit is the suite of predictive modelling methods. Accordingly, the course will develop your literacy in the strengths, characteristics and correct application of a range of predictive modelling techniques, from relatively simple linear models through to complex and powerful Random Forests, Support Vector Machines, Decision Trees, Tree Boosting Machines and Neural Networks will be covered along the way.

It will also teach you the correct framing of predictive modelling problems, suitably preparing data, evaluating model accuracy and stability, interpreting results and interrogating models.

The two key styles of predictive modelling – operational for targeting and explanatory for insights – will be described and distinguished.

As well as predictive modelling, the course will cover a range of other key data mining tools, including:

  • Data exploration and visualisation: univariate summaries, correlation matrices, heat maps, hierarchical clustering.
  • Cluster analysis – used for customer segmentation and anomaly detection
  • Other “unsupervised” outlier detection tools.

This course will primarily be taught using Rattle, a graphical interface for predictive modelling and data science in R. You will be exposed to “Big Data” techniques as applied to machine learning and deployed on Cloud Computing platforms.

Additional topics

The following additional topics may be covered depending on the pace and interests of the class:

  • Link and network analysis visualisation – which provide a simple and compelling way to communicate and analyse relationships, and are commonly applied in forensics, human resources and law enforcement.
  • Association analysis – used in retail market basket analysis and the assessment of risk groupings.
  • Frequent item set analysis.


Day 1

  • An overview of key terms: what do data sciencemachine learningAI and deep learning actually mean?
  • An intuitive and original introduction to what a machine learning model is, and what it does.
  • Practical exercise: Exploratory data analysis–summaries, visualisation, bar charts, pair plots and correlation plots.
  • Key terms: What is data? What is a model? What is a record? Field. Training set. Target variable. Missing value.
  • Introduction to predictive modelling: What is a decision tree model, how is one built, how does it make predictions and what else can be done with it?
  • Practical exercise: Building a decision tree model for classification.
  • Decision trees for regression (estimation of amounts), and practical exercise.
  • Linear regression models, and practical exercise.
  • Generalised linear models (logistic regression) for classification, and practical exercise.
  • Most important part of the course: How are predictive models evaluated? What is the KPI of predictive modelling?
  • What is the one thing that all practitioners, managers and stakeholders of machine learning must know? And what makes the definition, measurement and improvement of this KPI tricky ?
  • An intuitive, visual explanation of the problem of overfitting and the importance of out-of-sample testing.
  • Creating training/validation spits.
  • Using out-of-sample testing to evaluate models and select a final model.
  • The importance of a three-way training/validation/test split.
  • Accuracy measures for classification modelling.
  • Practical exercises: build multiple classification models, assess them on out-of-sample data and select the best final model out of a range of models including random forests, gradient boosting and support vector machines. Repeat as a model optimisation task to build the most accurate possible decision tree.
Day 2
  • Model deployment. Practical exercise: make new predictions on a developed model.
  • Model stability and degradation: the importance of rebuilding models and out-of-time testing
  • Advanced classification topics: selecting a classification threshold using ROC curve charts.
  • Calculation of the area under the ROC curve as a classification error measure.
Advanced topics
  • K-fold cross-validation: the “industry standard” in modern machine learning model evaluation.
  • Random forests: a powerful, simple to use, and reliable modelling method. How does it work? What are its unique strengths ?
  • Practical exercise with random forest.

Early bird pricing is available until 2 weeks prior.

This is an AlphaZetta public course – group discounts are available during the Early bird period (up to 2 weeks prior): 5% for 2–4 people, 10% for 5–6 people, 15% for 7–8 people, and 20% for 9 or more people. Discounts are calculated during checkout.

Course Booking Terms and Conditions

Additional Information

Audience C-Suite, Management, Business, Expert
This course is suitable for anyone in management, administrative, product, marketing, finance, risk and IT roles who works with data and wants to become acquainted with modern data analysis tools.
Prerequisites No prior knowledge of R is required to take this course. However, students should have completed or have equivalent knowledge to the course Data Literacy for Everyone.
  • Learn fundamentals of predictive modelling and experience using a range of methods.
  • Have improved their ability to assess the effectiveness and fitness for purpose of any predictive modelling tool or technique.
  • Have experience with a range of unsupervised data techniques.
  • Be exposed to Big Data and Cloud Computing applications.
Format Class
Duration 2 days
Course Author Dr Eugene Dubossarsky
Trainer Courses are taught by Dr Eugene Dubossarsky and/or his hand-picked team of highly skilled instructors.
Delivery Method In-person at AlphaZetta Academy locations or on-premise for corporate groups

Meals and refreshments

Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.


Use academy@alphazetta.ai to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.


Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.

Cancellations and refunds

You can get a full refund if you cancel 14 days or more before the course starts. No refunds will be issued for cancellations made less than 14 days before the course starts.

Frequently asked questions (FAQ)

Do I need to bring my own computer?
There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.

Here’s what David Andersen thought of this course in a short video:

Private and Corporate Training

In addition to our public seminars, workshops and courses, AlphaZetta Academy can provide this training for your organisation in a private setting at your location or ours. Please enquire to discuss your needs.

Enquire Now

Scheduled Public Courses

Private and Corporate Training

In addition to our public seminars, workshops and courses, AlphaZetta Academy can provide this training for your organisation in a private setting at your location or ours. Please enquire to discuss your needs.

Enquire Now

This is one of our core courses and is part of the Executive, Data Engineering, Data Governance, AI Engineering and Data Science core curricula and it’s an elective in the Data Culture curriculum.


Eugene’s courses are not your standard technical courses where you learn how to put data into a model and get a result. The real life experiences – warts and all – he brings to the instruction mean that attendees walk away with a better understanding of the real life challenges of analytics as well as the technical know-how. We routinely send our team members on these courses to help them get the capabilities that really help our clients get better insights from their data.

James Beresford, Director, Agile BI

Eugene’s introductory course to data science was outstanding. I found the subject matter and delivery fascinating, accessible and informative. I found Eugene approachable, interesting to listen to and excellent at simplifying complex concepts. I highly recommend this course for anyone who wants to know what data science—and all the buzz surrounding it!—are all about.

C.T. Johnson, Director, Statecraft