NZ courses - Presciient

Business analytics, big data and data science

The human side of analytics capability

Learn from an analytics leader with Dr Eugene Dubossarsky’s highly regarded courses in data science, machine learning and big data.

(Want to know what our students are saying? Check our testimonials page…)

Click on the city and date links below for further details including pricing, venue and course registration.

For any enquiries, please email enquiries@presciient.com, or call 0800-424282 (toll-free).

Predictive analytics, machine learning, data science and AI

Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Introduction to Data Science” and “Introduction to Machine Learning”.

Introduction to R and data visualisation

R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work.

Advanced masterclass 2: Random forests

Explore the many unique applications and extensions of the randomForest package, many of which are implemented in R, so you can easily solve problems not susceptible to other methods, including deep learning.

Learn about the random forest algorithm and a wide range of its applications in areas including fraud, risk and outlier detection, along with associated packages and extensions that enable visualisations and other features. You’ll also learn how to address the common challenge of dealing with small data sets and classes.

Advanced R

This class builds on “Introduction to R” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable. The course will also provide a deeper view of functional programming in R, which also allows cleaner and more powerful coding, as well as R Markdown, R Notebooks, and the shiny package for interactive documentation, browser-based dashboards and GUIs for R code.

NEW! Text and language analytics

Text analytics is a crucial skill set in nearly all contexts where data science has an impact, whether that be customer analytics, fraud detection, automation or fintech. In this course, you will learn a toolbox of skills and techniques, starting from effective data preparation and stretching right through to advanced modelling with deep-learning and neural-network approaches such as word2vec.

Introduction to data science

Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Machine Learning”.

Fraud and anomaly detection

This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods.

Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data.

A basic knowledge of R and predictive analytics is advantageous.

NEW! Data literacy for everyone

With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. This course for managers and workers without a strong quantitative background introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.

Introduction to Python for data analysis

Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.

Advanced machine learning masterclass

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. This course is also advertised as “Kaggle Boot Camp”.

Forecasting and trend analytics

This course is an intuitive introduction to forecasting and analysis of time-series data. We will review a range of standard forecasting methods, including ARIMA and exponential smoothing, along with standard means of measuring forecast error and benchmarking with naive forecasts, and standard pre-processing/de-trending methods such as differencing and missing value imputation. Other topics will include trend/seasonality/noise decomposition, autocorrelation, visualisation of time series, and forecasting with uncertainty.

NEW! Quantum computing

This is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.

Introduction to machine learning

Data science, predictive modelling and big data skills are of vital and growing importance in commercial, government, and not-for-profit contexts, particularly for managers and those in risk, customer and IT functions. Learn the fundamentals of predictive modelling, including coverage of generalised linear models, support vector machines, decision trees, and tree boosting machines. This course also covers a range of other key data mining tools, including cluster analysis. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Data Science”.

Kaggle boot camp: Feature engineering and advanced algorithms

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Note: This is an alternative title for the course “Advanced Machine Learning Masterclass”.

Predictive analytics, machine learning, data science and AI

Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Introduction to Data Science” and “Introduction to Machine Learning”.

NEW! Data literacy for everyone

With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. This course for managers and workers without a strong quantitative background introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.

Introduction to R and data visualisation

R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work.

Introduction to Python for data analysis

Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.

Advanced masterclass 2: Random forests

Explore the many unique applications and extensions of the randomForest package, many of which are implemented in R, so you can easily solve problems not susceptible to other methods, including deep learning.

Learn about the random forest algorithm and a wide range of its applications in areas including fraud, risk and outlier detection, along with associated packages and extensions that enable visualisations and other features. You’ll also learn how to address the common challenge of dealing with small data sets and classes.

Advanced machine learning masterclass

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. This course is also advertised as “Kaggle Boot Camp”.

Advanced R

This class builds on “Introduction to R” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable. The course will also provide a deeper view of functional programming in R, which also allows cleaner and more powerful coding, as well as R Markdown, R Notebooks, and the shiny package for interactive documentation, browser-based dashboards and GUIs for R code.

Forecasting and trend analytics

This course is an intuitive introduction to forecasting and analysis of time-series data. We will review a range of standard forecasting methods, including ARIMA and exponential smoothing, along with standard means of measuring forecast error and benchmarking with naive forecasts, and standard pre-processing/de-trending methods such as differencing and missing value imputation. Other topics will include trend/seasonality/noise decomposition, autocorrelation, visualisation of time series, and forecasting with uncertainty.

NEW! Text and language analytics

Text analytics is a crucial skill set in nearly all contexts where data science has an impact, whether that be customer analytics, fraud detection, automation or fintech. In this course, you will learn a toolbox of skills and techniques, starting from effective data preparation and stretching right through to advanced modelling with deep-learning and neural-network approaches such as word2vec.

NEW! Quantum computing

This is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.

Introduction to data science

Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Machine Learning”.

Kaggle boot camp: Feature engineering and advanced algorithms

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Note: This is an alternative title for the course “Advanced Machine Learning Masterclass”.

Introduction to machine learning

Data science, predictive modelling and big data skills are of vital and growing importance in commercial, government, and not-for-profit contexts, particularly for managers and those in risk, customer and IT functions. Learn the fundamentals of predictive modelling, including coverage of generalised linear models, support vector machines, decision trees, and tree boosting machines. This course also covers a range of other key data mining tools, including cluster analysis. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Data Science”.

Fraud and anomaly detection

This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods.

Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data.

A basic knowledge of R and predictive analytics is advantageous.

Training for all R courses will be conducted with Microsoft R Open, the Enhanced Distribution of R. “Predictive Analytics, Machine Learning and Data Science for Big Data” and related courses, along with all advanced courses, will also include use of Azure ML, Microsoft's interactive machine learning platform in the cloud. Some Python training may be demonstrated using Microsoft Azure Notebooks or on the Microsoft Data Science Virtual Machine.

Contact us today for more information.

Here’s what students are saying about our courses

Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!

Damon Rasheed

CEO, Rate Detective

As a manager of analysts, I attended this course to deepen my understanding of the principles of predictive modelling in R, and I was absolutely satisfied with this course. Eugene’s explanation of the fundamentals and the theory behind the techniques was much clearer than any online resource I have come across. His knowledge of what he is teaching is first class and I would recommend Eugene to everyone who is interested in not just learning a technique, but developing true understanding.

Maz Dunic

Senior data analytics manager, Save the Children Australia

Eugene’s fraud and anomaly detection course is extremely valuable for anyone wishing to learn more about fraud detection using analytical techniques. Eugene’s ability to cater and tailor the course for all levels of experience is fantastic and much appreciated.

Hannah Sakai

Senior analyst, Insurance Australia Group (IAG)

Eugene is an excellent communicator with excellent presenting skills. He was able to break down and explain advanced statistical concepts and modelling … in a very clear and easy-to-understand manner. He encourages feedback and student input to tailor the course to his pupils’ needs … to highlight advanced statistical analyses that are best practice and produce superior results.
He also identifies common struggles or obstacles faced in practically applied statistical modelling. Eugene is very personable and engaging and … provides continued support through one-on-one or small group meetups with pupils that wish to discuss any challenges they face in their work. His passion and knowledge shines through his courses and his teaching.

Kathleen Riethmuller

Statistical analyst, QBE

Eugene belongs to a rare breed of folks who can do, and can also teach. Eugene has an outstanding ability to distil complicated concepts into bits of essence. Through Eugene’s course, I got to learn about and better understand how to work with data, industry practices, and myself. The course was delivered in a very engaging way. I had a great learning experience.

Adi Nagara

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

Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha" moments.

It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.

Charl Swart

Director of Business Operations, Unisys Credit Services

For someone who does not come from an IT background R is a terrifying program. Before doing the Introduction to R course I had previously done other courses in R but always found myself in over my head because they assumed a high level of program experience (even course that required no prior programming knowledge). This course is not like that at all. It starts at ground zero and teaches you everything you need to know to be able to use R confidently in your everyday workplace. It is a must attend for anyone who wants use R!

Alix Duncan

Eugene’s Data Science course really opened my eyes to how accessible the latest machine learning tools are. I won’t need to spend months learning a hard-core new discipline, and I can already think of ways to use this at work.

Alain LeBel

Quantitative analyst and software developer, Cooper Investors

Training

Presciient regularly runs courses in R and a range of data analytical disciplines in major Australian and Asian cities. Check the links below for a full list of dates and courses currently available in each city, and reserve your place today with our convenient online booking facility.

Courses are conducted by Presciient director Dr Eugene Dubossarsky, a leader in the analytics field in Australia, with 20 years’ commercial data science experience. He is the head of the Sydney Data Science group (4,000+ members), the Sydney Users of R Forum (SURF) (2,000+ members), and Datapreneurs (500+ members). He is regularly invited to be a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics. Dr Dubossarsky also applies data science in an entrepreneurial setting, to financial trading and online startups, and is the creator of ggraptR, an interactive visualisation package in R.

See Eugene’s presentations and interviews in videos, podcasts and text here.

Business analytics consulting

Presciient consults on how to make better decisions, recognising that all decision making involves forecasting.

Our process involves using sophisticated techniques to measure, assess, and manage forecasts and the decisions that arise from them. We call this Decision Performance Management.

An integral part of this is Forecasting Performance Management. We advise on better forecasting methods, based on both statistics and human judgement, and on their implications for organisational structure and incentives. We consult on knowledge management as it pertains to forecasting.

The human side of data

Presciient specialises in the human side of business analytics, data science and big data. We provide services to build effective, valuable and recognised analytics capability within organisations large and small.

We assist our clients by training and mentoring their analytics teams, coaching executives in analytics management and value, and providing strategic advice in establishing and utilising analytics.

We also perform data analysis and implementation project for select clients.

We also conduct regular public training in various aspects of analytics, including predictive modelling, forecasting, forensics, fraud detection and security, visualisation and big data in Australia and Singapore.

About Dr Eugene Dubossarsky

Dr Eugene Dubossarsky is a leader in the analytics field in Australia, with 20 years’ commercial data science experience. He is the head of the Sydney Data Science group (4,000+ members), the Sydney Users of R Forum (SURF) (2,000+ members), and Datapreneurs (500+ members). He is regularly invited to be a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics. Eugene also applies data science in an entrepreneurial setting, to financial trading and online startups, and is the creator of ggraptR, an interactive visualisation package in R.

See Eugene’s presentations and interviews in videos, podcasts and text here.

Learn how you can build your analytics capability.