Data-Driven Management

2020-09-17T07:24:02+00:00Categories: AI Engineering Curriculum Electives, Data Engineering Curriculum Electives, Government, Data Science Curriculum, Data Governance Curriculum, Executive Curriculum, Dr Eugene Dubossarsky, Innovation & Tech Curriculum, All Academy Courses|Tags: , , , |

The Data-Driven Management 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.

Report Automation – Excel to PowerPoint with R

2020-10-05T07:06:32+00:00Categories: Dr Craig Savage, Data Engineering Curriculum Electives, Data Science Curriculum Electives, R, Data Visualisation, All Academy Courses|Tags: , , , |

Report automation can deliver powerful, time-saving results. This course teaches analytics professionals to automate the creation of PowerPoint packs from input Excel workbooks using R. Time is allotted for students to implement techniques taught so that, by the end of the course, students will have wrangled input data, created plots and tables, defined a PowerPoint template, and built a sample set of slides.

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

2020-10-19T06:44:36+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, 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.

Advanced Machine Learning Masterclass II: Random Forest

2020-09-18T05:59:23+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, tidyverse, R, Dr Eugene Dubossarsky, AI Engineering Curriculum, 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 Deep Learning

2020-10-19T07:42:37+00:00Categories: Keras, Innovation & Tech Curriculum Electives, Data Engineering Curriculum Electives, Tensorflow, Data Science Curriculum, Python, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|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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