Expert

Social Network Analysis: Practical Use Cases and Implementation

2019-10-24T04:51:00+00:00Categories: Data Governance Level 2, Predictive Analytics & AI, Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Governance Curriculum Electives, Executive Curriculum Electives, Marketing, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Stephen Brobst, Fraud and Security, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, AI Engineering Level 2, All Academy Courses|Tags: , , , |

Social networking via Web 2.0 applications such as LinkedIn and Facebook has created huge interest in understanding the connections between individuals to predict patterns of churn, influencers related to early adoption of new products and services, successful pricing strategies for certain kinds of services, and customer segmentation. We will explain how to use these advanced analytic techniques with mini case studies across a wide range of industries including telecommunications, financial services, health care, retailing, and government agencies. 

Data Science and Big Data Analytics: Leveraging Best Practices and Avoiding Pitfalls

2020-01-09T04:11:53+00:00Categories: Data Governance Level 2, Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Engineering Level 2, Data Management, Executive Level 2, Big Data, All Academy Courses|Tags: , , , , , , |

Data science is the key to business success in the information economy. This workshop will teach you about best practices in deploying a data science capability for your organisation. Technology is the easy part; the hard part is creating the right organisational and delivery framework in which data science can be successful in your organisation. We will discuss the necessary skill sets for a successful data scientist and the environment that will allow them to thrive. We will draw a strong distinction between “Data R&D” and “Data Product” capabilities within an enterprise and speak to the different skill sets, governance, and technologies needed across these areas. We will also explore the use of open data sets and open source software tools to enable best results from data science in large organisations. Advanced data visualisation will be described as a critical component of a big data analytics deployment strategy. We will also talk about the many pitfalls and how to avoid them.

Data Governance 1

2020-01-09T04:14:59+00:00Categories: Data Culture Level 1, Data Culture Electives, Government, Data Science Curriculum, Data Governance Curriculum, Data Science Level 1, Executive Curriculum, Mark Burnard, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Governance Level 1, AI Engineering Curriculum, Financial Risk, Data Engineering Level 1, AI Engineering Level 1, Executive Level 1, All Academy Courses, Innovation & Tech (CTO) Level 1|Tags: , , , |

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 CDO to executive to 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.

Advanced Python 2

2019-10-18T03:25:47+00:00Categories: Data Science Curriculum Electives, Python, Level 3, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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 Python 1

2019-10-18T03:24:37+00:00Categories: Level 2, Data Science Curriculum, Python, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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 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 R 1

2019-10-18T03:26:29+00:00Categories: Level 2, Data Science Curriculum, tidyverse, Shiny, 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 Machine Learning Masterclass 2: Random Forests

2019-11-29T04:49:19+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 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 1

2019-10-17T03:06:50+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.

Deep Learning and AI

2019-10-17T05:12:36+00:00Categories: Keras, Tensorflow, Level 2, Data Science Curriculum, Python, Data Engineering Curriculum, Dr Eugene Dubossarsky, All Academy Courses|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.