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Business analytics, AI 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 academy.anz@alphazetta.ai, or call +61 4 1457 3322 (Australia and international) or 0800-424282 (New Zealand, 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 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.

NEW! Soft skills for analytics professionals and data scientists

This course is for specialists working in the data domain. It teaches some of the vital skills that are not part of the formal training of quantitative professionals, which are essential in the modern workplace and crucial to the success of analytics efforts as well as the careers of analytics professionals.

Issues covered include effective communication, including presentation and communication skills, “storytelling”, and effective listening and elicitation. Technical methods will be presented in the context of their communication value.

The course also shares key insights and “trade secrets” that have served Eugene Dubossarsky well over decades of consulting, enterprise and startup work. These include effective ways to structure teams, projects, and analytics functions and careers, as well as “managing up”, branding and work style.

SPECIAL VIP GUEST! Data transformation and analysis using Apache Spark

With big data expert and author Jeffrey Aven. The first module in the “Big Data Development Using Apache Spark” series, this course provides a detailed overview of the spark runtime and application architecture, processing patterns, functional programming using Python, fundamental API concepts, basic programming skills and deep dives into additional constructs including broadcast variables, accumulators, and storage and lineage options. Attendees will learn to understand the Spark framework and runtime architecture, fundamentals of programming for Spark, gain mastery of basic transformations, actions, and operations, and be prepared for advanced topics in Spark including streaming and machine learning.

SPECIAL VIP GUEST! Advanced analytics using Apache Spark

With big data expert and author Jeffrey Aven. The third module in the “Big Data Development Using Apache Spark” series, this course provides the practical knowledge needed to perform statistical, machine learning and graph analysis operations at scale using Apache Spark. It enables data scientists and statisticians with experience in other frameworks to extend their knowledge to the Spark runtime environment with its specific APIs and libraries designed to implement machine learning and statistical analysis in a distributed and scalable processing environment.

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.

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.

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 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”.

NEW! Data driven decision-making for executives

This course is for executives and managers who want to leverage analytics to support their most vital decisions and enable better decision-making at the highest levels. It empowers senior executives with skills to make more effective use of data analytics. It covers contexts including strategic decision-making and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards. They will also be taught to work with analysts as effective customers.

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.

SPECIAL VIP GUEST! Stream and event processing using Apache Spark

With big data expert and author Jeffrey Aven. The second module in the “Big Data Development Using Apache Spark” series, this course provides the knowledge needed to develop real-time, event-driven or -oriented processing applications using Apache Spark. It covers using Spark with NoSQL systems and popular messaging platforms like Apache Kafka and Amazon Kinesis. It covers the Spark streaming architecture in depth, and uses practical hands-on exercises to reinforce the use of transformations and output operations, as well as more advanced stream-processing patterns.

NEW! Blockchain fundamentals and applications

Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype.

We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain.

The course does not require a technical background.

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.

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.

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”.

Data governance

As data becomes more and more critical to the effective operation of organisations and to their performance in an increasingly competitive landscape, the efficient and effective management of that data becomes crucial. Data governance refers to the processes and skills required to effectively manage data, whether big or small, traditional or digital.

This two-day course provides an informed, realistic and comprehensive foundation for establishing best-practice data governance in your organisation. Suitable for every level from chief data officer (CDO) to executive and data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a data governance strategy and roadmap.

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.

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”.

NEW! Data driven decision-making for executives

This course is for executives and managers who want to leverage analytics to support their most vital decisions and enable better decision-making at the highest levels. It empowers senior executives with skills to make more effective use of data analytics. It covers contexts including strategic decision-making and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards. They will also be taught to work with analysts as effective customers.

NEW! Soft skills for analytics professionals and data scientists

This course is for specialists working in the data domain. It teaches some of the vital skills that are not part of the formal training of quantitative professionals, which are essential in the modern workplace and crucial to the success of analytics efforts as well as the careers of analytics professionals.

Issues covered include effective communication, including presentation and communication skills, “storytelling”, and effective listening and elicitation. Technical methods will be presented in the context of their communication value.

The course also shares key insights and “trade secrets” that have served Eugene Dubossarsky well over decades of consulting, enterprise and startup work. These include effective ways to structure teams, projects, and analytics functions and careers, as well as “managing up”, branding and work style.

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.

SPECIAL VIP GUEST! Data transformation and analysis using Apache Spark

With big data expert and author Jeffrey Aven. The first module in the “Big Data Development Using Apache Spark” series, this course provides a detailed overview of the spark runtime and application architecture, processing patterns, functional programming using Python, fundamental API concepts, basic programming skills and deep dives into additional constructs including broadcast variables, accumulators, and storage and lineage options. Attendees will learn to understand the Spark framework and runtime architecture, fundamentals of programming for Spark, gain mastery of basic transformations, actions, and operations, and be prepared for advanced topics in Spark including streaming and machine learning.

SPECIAL VIP GUEST! Stream and event processing using Apache Spark

With big data expert and author Jeffrey Aven. The second module in the “Big Data Development Using Apache Spark” series, this course provides the knowledge needed to develop real-time, event-driven or -oriented processing applications using Apache Spark. It covers using Spark with NoSQL systems and popular messaging platforms like Apache Kafka and Amazon Kinesis. It covers the Spark streaming architecture in depth, and uses practical hands-on exercises to reinforce the use of transformations and output operations, as well as more advanced stream-processing patterns.

SPECIAL VIP GUEST! Advanced analytics using Apache Spark

With big data expert and author Jeffrey Aven. The third module in the “Big Data Development Using Apache Spark” series, this course provides the practical knowledge needed to perform statistical, machine learning and graph analysis operations at scale using Apache Spark. It enables data scientists and statisticians with experience in other frameworks to extend their knowledge to the Spark runtime environment with its specific APIs and libraries designed to implement machine learning and statistical analysis in a distributed and scalable processing environment.

NEW! Blockchain fundamentals and applications

Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype.

We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain.

The course does not require a technical background.

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.

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.

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.

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.

Data governance

As data becomes more and more critical to the effective operation of organisations and to their performance in an increasingly competitive landscape, the efficient and effective management of that data becomes crucial. Data governance refers to the processes and skills required to effectively manage data, whether big or small, traditional or digital.

This two-day course provides an informed, realistic and comprehensive foundation for establishing best-practice data governance in your organisation. Suitable for every level from chief data officer (CDO) to executive and data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a data governance strategy and roadmap.

Contact us today for more information.

The reasons to choose Presciient

 

Presciient training delivers a range of unique benefits that you’re not likely to find with other providers. We go beyond teaching processes to supplementing that knowledge with thinking skills that enable participants to understand, improvise, troubleshoot and manage the data-science function.

Our courses deliver some of the knowledge that you’d typically get only through intensive study of mathematical theory in a university degree—but in a much shorter time frame, and in a much more focused, intuitive, and accessible way that’s suitable for people without any statistical background, while also offering the same advantages for those already well-versed in maths.

Courses are highly visual, keeping the use of formulae to a minimum and transferring statistical intuition in its essence—the Zen of data science. They are also a taste of what it’s like to be mentored in data science, coming from industry veterans with deep knowledge of how to best operate as a data scientist, and of what organisations really need from the discipline.

The reasons to choose Presciient

Presciient training delivers a range of unique benefits that you’re not likely to find with other providers. We go beyond teaching processes to supplementing that knowledge with thinking skills that enable participants to understand, improvise, troubleshoot and manage the data-science function.

Our courses deliver some of the knowledge that you’d typically get only through intensive study of mathematical theory in a university degree—but in a much shorter time frame, and in a much more focused, intuitive, and accessible way that’s suitable for people without any statistical background, while also offering the same advantages for those already well-versed in maths.

Courses are highly visual, keeping the use of formulae to a minimum and transferring statistical intuition in its essence—the Zen of data science. They are also a taste of what it’s like to be mentored in data science, coming from industry veterans with deep knowledge of how to best operate as a data scientist, and of what organisations really need from the discipline.

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 leader in the analytics field in Australia, with over 20 years’ commercial data science experience. He is a Founding Partner of AlphaZetta, head of the AlphaZetta global training Academy as well as AlphaZetta's Chief Data Scientist. He is also the founder of the Australia–New Zealand Data Analytics Network, with over 17,000 members, and the head of the Data Science Sydney group (6,000+ members) and Big Data analytics Sydney (6,700+ 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, and serves as an advisory board member for listed companies, advising on AI and Data Science. 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.


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