A course in analytics is a great place to
start or to continue your development.
The most effective businesses make their decisions based on data and evidence, rather than unfounded gut feelings. This course covers the statistical methods that analysts need to move from simple reporting on business problems to extracting insight to solve business problems. Delegates will learn how to use modern data analytics tools to generate descriptive statistics, perform statistical testing and build statistical models. On returning to work after completing this course delegates will immediately be able to make a difference to the way that their organisations make decisions.
Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R and python languages are some of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions.
The use of analytics is widespread in business and for those without analytics training running analytics projects and building analytics teams can be daunting. This short, focused course will explain how analytics can be used in an organisation, how to run and manage an analytics project, and how to build a successful analytics team.
The companies using analytics most successfully understand that using sophisticated analytics approaches to unlock insights from data is only half the job. Communicating these insights to all of the different parts of an organisation is just as important as doing the actual analysis. Visualising data, and analytics results, is one of the most effective ways to achieve this. This course will cover the theory of data visualisation along with practical skills for creating compelling visualisations from data.
Predictive analytics applications use machine learning to build predictive models for applications including price prediction, risk assessment, and predicting customer behaviour. Based on the trainers’ book, “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” (www.machinelearningbook.com) this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Many business needs in the realm of analytics can be addressed using network analysis. Examining data for communities or matrices of co-occurring elements, events or people can be useful in detecting items as diverse as influencers, fraud and communities of buyers. This course provides in-depth exposure to techniques that can be used to model networks within data.
Time series data arises in applications from finance to personal activity monitoring and has unique characteristics that demand the use of specialised techniques. The course covers the fundamentals of modelling time series data and focuses on the application of the main model types used to analyse univariate time series: simple linear regression, exponential smoothing and autoregressive integrated moving average with exogenous variables (ARIMAX). This course can be delivered in R, Python, or SAS.
While many machine learning tasks, such as propensity modelling, have become standardised to the point of near automation, detecting anomalies in large complex datasets remains a fundamental challenge often requiring bespoke, creative solutions. There are, however, a core set of techniques and design patterns that can be built upon for anomaly detection problems in domains such as fraud detection, risk identification, and classification of rare events. Through presentations, real world examples, discussions, and workshops this workshop introduces the most important of these. This course can be delivered in R, Python, or SAS.
This course focuses on applying natural language processing tools and techniques to extract insights from large collections of text data. It has been designed to guide delegates through the most important topics in NLP, and how they should be applied to build real-world-relevant solutions. This course is delivered in Python.
This course focuses on using machine learning techniques based on large artificial neural networks to perform prediction and generation tasks. These are often referred to as deep learning models and their introduction in recent years, coupled with advances in computational hardware and the scale of datasets currently available to us, has led to step changes in the performance of machine learning models. This course has been designed to guide delegates through the most important topics in deep learning, and how they should be applied to build real-world-relevant solutions. This course is delivered in Python.
Processing high-volume and high-velocity datasets remains a challenge for conventional data processing platforms and requires specific tools and technologies beyond the standard data analytics stack. This course introduces the key tools and architectures that are used to manage and process high volume and high velocity datasets. Tools and architectures that will be explored include Apache Hadoop, Apache Spark and more. This course will equip students to build modern big data solutions to solve real-world problems.
Project management is an in-depth skill that is required in all aspects of business. Analytics projects have their own challenges demanding an analytics-oriented project management approach. This course describes good general project management practice and methodologies. It then focuses on providing the scaffolding for analytics project management specifically. It will introduce our Agile Analytics Framework which incorporates elements of Problem Solving, Agile and CRISP-DM methodologies to ensure that every data project delivers positive business impact.
Part of the challenge facing Data Analytics teams is how to engage and influence the wider organisation around them. Internal customers don’t always know quite what to ask for, and it’s easy to get carried away focusing on what you think they need. This workshop is designed to support individuals and teams in how best to interact with each other and the organisation around them using sound problem solving and project management skills alongside an awareness of stakeholder management and communication.