Intro to R (+ data visualisation)

2020-09-18T06:36:52+00:00Categories: Level 1, Data Culture Electives, Impact, Data Science Curriculum, R, Data Visualisation, Data Engineering Curriculum, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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.

Fraud and Anomaly Detection

2020-09-18T03:57:24+00:00Categories: Level 2, Data Science Curriculum Electives, Fraud and Security, R, Dr Eugene Dubossarsky, Financial Risk, All Academy Courses|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.

Text and Language Analytics

2020-03-16T00:52:52+00:00Categories: AI Engineering Curriculum Electives, Level 2, Data Science Curriculum Electives, R, R Electives, Dr Eugene Dubossarsky, All Academy Courses|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

2020-09-23T23:06:56+00:00Categories: AI Engineering Curriculum Electives, Data Science Curriculum Electives, R, Dr Eugene Dubossarsky, All Academy Courses|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 R 1

2020-07-10T07:50:39+00:00Categories: Level 2, Data Science Curriculum, tidyverse, Shiny, R, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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

2019-10-18T03:26:41+00:00Categories: Data Science Curriculum Electives, tidyverse, R, Level 3, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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 Machine Learning Masterclass I

2020-09-18T05:35:18+00:00Categories: Level 2, Data Science Curriculum, tidyverse, R, Data Engineering Curriculum, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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.

Advanced Machine Learning Masterclass II: Random Forest

2020-09-18T05:59:23+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, tidyverse, R, Data Engineering Level 2, Dr Eugene Dubossarsky, AI Engineering Curriculum, AI Engineering Level 2, All Academy Courses|Tags: , |

This advanced machine learning masterclass will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to the methods in random forest allows the user [...]

Advanced Fraud and Anomaly Detection

2019-11-29T04:48:52+00:00Categories: AI Engineering Curriculum Electives, Data Science Curriculum Advanced Electives, Fraud and Security, R, Level 3, Dr Eugene Dubossarsky, Financial Risk, All Academy Courses|Tags: , |

The detection of anomalies is one of the most eclectic and difficult activities in data analysis. This course builds on the basics introduced in the earlier course, and provides more advanced methods including supervised and unsupervised learning, advanced use of Benford’s Law, and more on statistical anomaly detection. Optional topics may include anomalies in time series, deception in text and the use of social network analysis to detect fraud and other undesirable behaviours.

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