Dr Eugene Dubossarsky

AlphaZetta Academy Course Author & Trainer

Dr Eugene Dubossarsky

Eugene Dubossarsky is Managing Partner of AlphaZetta Academy and 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 (3,000+ members), the Sydney Users of R Forum (1,900+ members), and Datapreneurs (400+ members). Eugene is regularly invited to be a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics. He 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.

Eugene is also our principal trainer, offering over 21 training courses in data science and related topics.

Data Visualisation and Communication

2021-09-08T03:57:01+00:00August 20th, 2020|Tags: , , , |

This course prepares data analytics professionals to communicate analytics results to business audiences, in a business context while being mindful of the skills, incentives, priorities and psychology of the audience. It also equips analysts [...]

Critical Thinking for Data Analytics

2021-09-08T03:54:32+00:00March 16th, 2020|Tags: |

This course is a vital first step in the data literacy journey, and the one that introduces the most vital and basic skills of the effective 21st century professional or leader. Working with data is not just about manipulating software tools : it is first and foremost about effective reasoning, using all available information. As such, this course loads the key “software” into the most vital hardware of the business - the human professional, enabling them to reason effectively with data, and thus realise the value that data analytics promises, deriving more reliable and correct insights and making better decisions.

Intro to R (+ data visualisation)

2021-04-13T04:19:20+00:00December 5th, 2018|Tags: , |

This R training 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. 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.

Intro to Python for Data Analysis

2021-03-16T06:59:15+00:00January 21st, 2019|Tags: , |

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.

Fundamentals of AI, Machine Learning, Data Science and Predictive Analytics

2023-09-01T06:34:13+00:00November 30th, 2018|Tags: , , , , |

This course is an intuitive, hands-on introduction to ai, data science and machine learning, it's your artificial intelligence 101. The training focuses on fundamentals and key skills, leaving you with a deep understanding of the core concepts of ai and 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. As our leading course, it has transformed the artificial intelligence (AI), machine learning (ML) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it.

AI and Data Science for Managers and Executives

2023-09-01T06:34:40+00:00July 20th, 2019|Tags: , , , |

Improve your project’s chance of success by avoiding common failures in AI and data science projects. This one-day workshop is aimed at current or aspiring leaders and managers of AI / machine learning teams and functions. The focus of the course is on the key concepts that are required to avoid the most common and far too frequent failures in AI projects and initiatives.

Data Literacy for Everyone

2023-09-01T06:35:01+00:00January 22nd, 2019|Tags: , , |

This course is for workers and managers without a strong quantitative background. It introduces a range of skills and applications related to data literacy for digital transformations and 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.

Data-Driven Decision-Making

2023-09-01T06:35:11+00:00December 1st, 2019|Tags: , , , |

The Data-Driven Decision-Making 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.

Fraud and Anomaly Detection

2021-03-12T03:57:53+00:00February 11th, 2019|Tags: , |

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 inter-operation 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.

Deep Learning and AI

2021-07-19T01:53:31+00:00February 28th, 2019|Tags: , |

This course is an introduction to the highly celebrated area of Neural Networks, popularised as “deep learning” and “AI”. The course will cover the key concepts underlying neural network technology, as well as the unique capabilities of a number of advanced deep learning technologies, including Convolutional Neural Nets for image recognition, recurrent neural nets for time series and text modelling, and new artificial intelligence techniques including Generative Adversarial Networks and Reinforcement Learning. Practical exercises will present these methods in some of the most popular Deep Learning packages available in Python, including Keras and Tensorflow. Trainees are expected to be familiar with the basics of machine learning from the Fundamentals course, as well as the python language.

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