Our Data Science Curriculum is comprehensive in its coverage of the many topics in the field. We offer starting points for all levels – from raw beginner to expert. Our curriculum is customisable for organisations with specific needs.
Courses are offered in online and face-to-face formats.
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.
With big data expert and author Jeffrey Aven. Learn how to develop applications using Apache Spark. The first module in the “Big Data Development Using Apache Spark” series, this course provides a detailed overview of the spark runtime and application architecture, processing patterns, functional programming using Python, fundamental API concepts, basic programming skills and deep dives into additional constructs including broadcast variables, accumulators, and storage and lineage options. Attendees will learn to understand the Apache Spark framework and runtime architecture, fundamentals of programming for Spark, gain mastery of basic transformations, actions, and operations, and be prepared for advanced topics in Spark including streaming and machine learning.
The second module in the “Big Data Development Using Apache Spark” series, this course provides the Spark streaming knowledge needed to develop real-time, event-driven or event-oriented processing applications using Apache Spark. It covers using Spark with NoSQL systems and popular messaging platforms like Apache Kafka and Amazon Kinesis. It covers the Spark streaming architecture in depth, and uses practical hands-on exercises to reinforce the use of transformations and output operations, as well as more advanced stream-processing patterns. With big data expert and author Jeffrey Aven.
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.
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.
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.
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.
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.
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.
This course describes the cultural and organisational aspects required for an organisation on the digital transformation path. A healthy corporate culture around data awareness is imperative to leverage the potential and value of data to the benefit of a company's business model. The organisation needs to reflect the culture and reward those who add value to a corporation by using data and analytics. Content presented explains personality and skill identification, how to prototype an agile analytics organisation and describe how to validate change capabilities, close gaps and execute a transition strategy.
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