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.

Text and Language Analytics

2021-10-22T01:05:45+00:00January 22nd, 2019|Tags: , |

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.

Forecasting and Trend Analysis

2021-03-12T03:57:54+00:00February 12th, 2019|Tags: , |

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.

Advanced Python 1

2021-03-12T03:57:58+00:00March 5th, 2019|Tags: , |

This class builds on the introductory Python class. Jupyter Notebook advanced use and customisation is covered as well as configuring multiple environments and kernels. The Numpy package is introduced for working with arrays and matrices and a deeper coverage of Pandas data analysis and manipulation methods is provided including working with time series data. Data exploration and advanced visualisations are taught using the Plotly and Seaborne libraries.

Advanced R 1

2021-03-12T03:57:58+00:00March 1st, 2019|Tags: , |

This class builds on “Intro to R (+data visualisation)” 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.

Advanced R 2

2021-03-12T03:57:58+00:00March 1st, 2019|Tags: , |

This course goes deeper into the tidyverse family of packages, with a focus on advanced data handling, as well as advanced data structures such as list columns in tibbles, and their application to model management. Another key topic is advanced functional programming with the purrr package, and advanced use of the pipe operator. Optional topics may include dplyr on databases, and use of rmarkdown and Rstudio notebooks.

Advanced Python 2

2021-04-13T04:06:24+00:00March 5th, 2019|Tags: , |

In the Advanced Python 2 course, you will learn advanced methods and packages for working with "big data" with Pandas. The course also covers using Dask for parallel computation. Machine learning is demonstrated with [...]

Advanced Machine Learning Masterclass I

2021-03-12T03:57:57+00:00March 1st, 2019|Tags: , |

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.

Quantum Computing

2021-03-12T03:57:56+00:00February 22nd, 2019|Tags: , , , |

This course 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.

Advanced Deep Learning

2021-07-26T01:19:34+00:00December 5th, 2018|Tags: , |

This course provides a more rigorous, mathematically based view of modern neural networks, their training, applications, strengths and weaknesses, focusing on key architectures such as convolutional nets for image processing and recurrent nets for text and time series. This course will also include use of dedicated hardware such as GPUs and multiple computing nodes on the cloud. There will also be an overview of the most common available platforms for neural computation. Some topics touched in the introduction will be revisited in more thorough detail. Optional advanced topics may include Generative Adversarial Networks, Reinforcement Learning, Transfer Learning and probabilistic neural networks.

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