General Analytics

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.

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Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates who are already familiar with analytics techniques and at least one programming language how to effectively use the programming language for three tasks: data manipulation and preparation, statistical analysis and advanced analytics (including predictive modelling and segmentation). 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.

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This course is for users who want to learn how to write SAS programs. It is the entry point to learning SAS programming and is a prerequisite to many other SAS courses. If you do not plan to write SAS programs and you prefer a point-and-click interface, you should attend the SAS Enterprise Guide 1: Querying and Reporting (EG7.1) course.

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This course is for users who do not have SAS programming experience but need to access, manage, and summarize data from different sources, and present results in reports and graphs. This course focuses on using the menu-driven tasks in SAS Enterprise Guide, the point-and-click interface to SAS, to create queries and reports. It does not address writing SAS code or statistical concepts. This course serves as a prerequisite for the SAS Enterprise Guide 2: Advanced Tasks and Querying (EG 7.1) course and for the Creating Reports and Graphs with SAS Enterprise Guide course. It also serves as a prerequisite for the SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression (EG 7.1) course, which teaches statistical concepts using SAS Enterprise Guide.

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This course is for those who need to learn data manipulation techniques using SAS DATA and procedure steps to access, transform, and summarize SAS data sets. The course builds on the concepts that are presented in the SAS Programming 1: Essentials course and is not recommended for beginning SAS software users.

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Overview: This course is a boot camp that covers the content of both SAS Data Integration Studio: Essentials and SAS Data Integration Studio: Additional Topics. It introduces and expands the knowledge of SAS Data Integration Studio and includes topics for registering sources and targets; creating and working with jobs; and working with transformations. This course also covers information on working with slowly changing dimensions, working with the Loop transformations, and defining new transformations.

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This course focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with macro code are processed.

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This course is for SAS programmers who prepare data for analysis. The comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation.

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Overview: This course is intended for experienced SAS Enterprise Guide users who want to learn more about advanced SAS Enterprise Guide techniques. It focuses on using the Query Builder within SAS Enterprise Guide, including manipulating character, numeric, and date values; converting variable type; and building conditional expressions using the Expression Builder. This course also addresses efficiency issues, such as joining tables and using a single query to group, summarize, and filter data.

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This course provides students with the essential knowledge to perform the job functions of a SAS platform administrator.

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Statistics

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.

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Overview: This course is designed as the first step for those who want to use JMP to manage, analyze, and explore data. Two case studies will be used to present typical data analysis issues that our students see. (The ecourse will have the same general content but with a different format.)

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Overview: This course teaches how to analyze data with a single continuous response variable using analysis of variance and regression methods. You learn how to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Important statistical concepts such as confidence intervals are also introduced.

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Management

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.

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Project management is an in-depth skill that is required in all aspects of business. Analytics projects have their own particular specialities 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. This course provides a ‘how to’ guide for those who will need to be actively involved in managing the project, as opposed to a general introduction for those who are building analytics teams (which is provided in our ‘Analytics for Managers’ course).

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Visualisation

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.

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The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to horse breeding.  Organisations spend a huge amount of recourses applying business intelligence and advanced analytics techniques to uncover these insights. Organisations doing this 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, reports and dashboards from data using SAS Visual Analytics.

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Predictive Analytics

So, you’ve learned about the power of analytics and advanced analytics techniques, but are you getting the most out of your data? Using examples from customer segmentation to sales forecasting, this course demystifies the techniques required to transform raw data into insight-filled data for driving your analytics solutions.

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Machine learning and predictive data analytics are fast becoming the best way for sophisticated organisations to use data to gain a competitive edge. 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.

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Machine Learning

So, you’ve learned about the power of analytics and advanced analytics techniques, but are you getting the most out of your data? Using examples from customer segmentation to sales forecasting, this course demystifies the techniques required to transform raw data into insight-filled data for driving your analytics solutions.

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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.

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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.

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Network Analysis

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.

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