Duration: 2 Days

Cost: €1,010

Technologies: Excel

Overview: 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.

Most importantly, though, delegates will learn how to effectively interpret the results of statistical analyses to make better business decisions.

Outcomes: After attending this course, delegates will understand how statistics can be used to provide valuable insight into their business, and be able to apply statistical methods to solve business problems. On returning to work delegates will immediately be able to make a difference to the way that their organisations make decisions.

Pre-requisites: To attend this course delegates should be competent in the use of spreadsheet software (for example Microsoft Excel). The course assumes no prior statistical knowledge.

Who: This course is suited to marketeers, business analysts, and researchers who are interested in increasing their statistical knowledge.

Outline: The course will explore the following topics through a series of lectures and interactive workshop sessions.

Summary statistics for both continuous data and categorical data

• Using and reporting confidence intervals
• Using hypothesis tests to answer business questions
• Using correlations to explore data relationships
• Simple prediction models
• Analysing categorical data

Duration: 2 Days

Cost: €1,400

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.

Learn how to

Use JMP software to:

• compare two means using a t-test
• generate and interpret an analysis of variance to compare more than two means
• analyze relationships between continuous variables using simple and multiple linear regression models
• perform an analysis of covariance to incorporate continuous and categorical predictors
• evaluate assumptions in statistical hypothesis testing.

Pre-requisites: Before attending the course, you should complete the JMP Software: A Case Study Approach to Data Exploration course or have equivalent experience.

This course uses JMP software.

Who: Analysts and researchers with some statistical knowledge

Outline:

Introduction to Statistics

• statistical concepts
• descriptive statistics and some of their graphs
• inferential statistics
• hypothesis tests

Comparing Means

• one-sample t-test
• paired t-test (self-study)
• two-sample t-test

Analysis of Variance

• one-way ANOVA with two groups
• one-way ANOVA with more than two groups
• fitting an N-way ANOVA model
• contrasts in N-way ANOVA (self-study)
• power and sample size

Simple Linear Regression

• exploratory data analysis
• simple linear regression
• polynomial regression

Multiple Regression

• fitting a multiple regression model
• fitting a multiple regression model with interactions
• generating and comparing candidate models

Regression Diagnostics

• evaluating assumptions
• influential observations
• collinearity

ANCOVA (Self-Study)

• fitting an analysis of covariance (ANCOVA) model

Duration: 3 days
Cost: €1422

Overview: 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.

Pre-requisites: Before attending this course, you should have at least six months of experience writing SAS programs or have completed the SAS Programming 1: Essentials course and used SAS for at least one month. Specifically, you should be able to

• submit a SAS program
• diagnose and correct syntax errors
• examine descriptor and data portions of a SAS data set
• access SAS data libraries
• read and create SAS data sets
• read Excel spreadsheets
• read delimited raw data files
• examine data errors when reading raw data files
• use SAS procedures to validate data
• clean invalid data
• create variables
• combine SAS data sets
• use global statements
• use labels and formats, including user-defined formats
• subset observations
• produce summary reports using the FREQ and MEANS procedures.

This course addresses Base SAS software.

This course is appropriate for students who are using SAS 9 software.

Who: Anyone starting to write SAS programs.

Outline: The course will explore the following topics through a series of interactive workshop sessions:

• control SAS data set input and output
• combine SAS data sets
• summarize, read, and write different types of data
• perform DO loop and SAS array processing
• transform character, numeric, and date variables.

Duration: 3 Days

Overview

Introduction

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.

All delegates receive a free copy of the book “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies”

Presented by Aoife D’Arcy  

Aoife has spent the last 15 years developing analytical models and processes for major national and international companies in banking, finance, insurance, gaming and manufacturing. Aoife has developed particular expertise in customer insight analytics, fraud analytics, and risk analytics.

Aoife founded The Analytics Store in 2009 to peruse her passionate belief in the importance of developing in-house analytics talent in organisations. Aoife works with organisations to help them build world class analytics teams and processes through a unique mix of training, consultancy and mentoring.

Aoife is a co-author of the textbook “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples & Case Studies” published in 2015 with MIT Press.

Learn how to:

This course has been designed to guide delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models using SAS. After completing the course delegates will:

• Understand how to frame a business problem as a predictive analytics problem
• Understand the fundamental theories of machine learning, and the most important machine learning approaches
• Be familiar with a wide range of applications of predictive data analytics and machine learning, including the limitations of machine learning
• Be comfortable analysing the quality of datasets for machine learning models
• Have an awareness of how SAS can be used to build predictive analytics models using machine learning techniques
• Be fully prepared to understand newly emerging advanced topics in machine learning

Who Should attend

This course is aimed at people in a technical role who want to fully understand and use machine learning based predictive analytics techniques. This course is for you if:

• you need to learn about the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models
• you need to learn how to apply detailed examples and real-world case studies using SAS technology.
• you like learning from experts, in an instructor led class room environment
• you are interested in understanding sophisticated machine learning theories, and how they are applied to enable best business practice

Prerequisites

To attend this course delegates should be familiar with basic statistical concepts (such as mean, standard deviation, and correlation) and comfortable with data manipulation tools such as spreadsheets and databases. Some knowledge of SAS would be useful but not essential.

Course Outline

The course will cover the following key topics through a series Lectures, demos and interactive workshop sessions.

Day 1 Topics:

Introduction

• The analytics process:
  • Data – Insight – Decision
  • Crisp-DM
• What is predictive data analytics and what is it used for?
• What is machine learning?
• Training machine learning models – inductive bias, generalisation, overfitting and underfitting

Data Preparation for Machine Learning

• Review of data analysis and data visualisation
• Feature engineering
• Assessing data quality
• Standard data manipulation techniques and their pros and cons,
  • Binning
  • Imputations
  • Standardization

Demos & Workshop: Applying these techniques in SAS

Day 2 Topics

Information-based Learning

• Fundamentals
  • Decision Trees
  • Shannon’s Entropy Model
  • Information Gain
• Standard Approach: The ID3 Algorithm
• A Worked Example: Predicting Vegetation Distributions
• Alternative Feature Selection Metrics
• Handling Continuous Descriptive Features
• Noisy Data, Overfitting and Tree Pruning

Model Evaluation

• Standard Approach: Measuring Misclassification Rate on a Hold-out Test Set
• Designing Evaluation Experiments
  • Hold-out Sampling
  • k-Fold Cross Validation
• Performance Measures: Categorical Targets
  • Average Class Accuracy
  • Performance Measures: Multinomial Targets
• Performance Measures: Prediction Scores
  • Receiver Operating Characteristic Curves
  • Gini and KS Statistics
  • Measuring Gain and Lift
• Performance Measures: Continuous Targets

Demos & Workshop: Applying these techniques in SAS

Day 3 Topics

Other Machine Learning Method Explored

• Error Based Learning

• • • Multiple Linear Regression
    • Logistic Regression
    • Neural Networks

• Ensemble Model

• • • Bagging
    • Boosting
    • Random Forests

• Non-linear Models & Support Vector Machines

Demos & Workshop: Applying these techniques in SAS

Duration: 2 Days

Cost: €1010

Technologies: Tableau

Overview: 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. The companies 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 from data.

Outcomes: At completion, delegates will understand how data visualisations can be best used to communicate actionable insights from data and be competent with the tools required to do it.

Pre-requisites: To attend this course delegates should be competent in the use of data analysis tools such as reporting tools, spreadsheet software or business intelligence tools.

Who: This course is aimed at anyone currently working with data who is interested in using data visualisation to more effectively communicate their results.

Outline: The course will explore the following topics through a series of interactive workshop sessions:

• Fundamentals of data visualisation
• Data characteristics & dimensions
• Mapping visual encodings to data dimensions
• Colour theory
• Graphical perception & communication
• Interaction design
• Visualisation different characteristics of data: trends, comparisons, correlations, maps, networks, hierarchies, text
• Designing effective dashboards

Title: SAS Data Integration Studio: Fast Track

Duration: 4 Days

Cost: €3,500

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.

Learn how to:

• register source data and target tables
• create jobs and explore the functionality of the job editor
• work with many of the various transformations
• work with slowly changing dimensions
• work with Loop transformations
• create new transformations
• examine impact analysis
• examine exporting and importing of metadata
• establish checkpoints in job flow
• deploy jobs for scheduling
• deploy jobs as SAS Stored Processes.

Pre-requisites: Before attending this course, you should have experience with

• SAS programming basics
• SQL processing
• the SAS macro facility.

You can gain this experience by completing the SAS Programming 1: Essentials, SAS SQL 1: Essentials, and SAS Macro Language 1: Essentials courses.

This course uses SAS Data Integration Studio, SAS Data Quality Solution, SAS Analytics Platform software.

Who: Data integration developers and data integration architects

Outline:

Introduction

• exploring the platform for SAS Business Analytics
• introduction to the Data Management applications
• introduction to the classroom environment and the course tasks

Working with Change Management

• introduction to change management
• establishing a change management environment (Self-Study)

Creating Metadata for Source Data

• setting up the environment
• registering source data metadata

Creating Metadata for Target Data

• registering target data metadata
• importing metadata

Creating Metadata for Jobs

• introduction to jobs and the job editor
• using the Join transformation

Orion Star Case Study

• defining and loading the customer dimension table
• defining and loading the organization dimension table
• defining and loading the time dimension table

Additional Features for Jobs

• importing SAS code
• propagation and mapping
• chaining jobs
• performance statistics
• metadata reports

Working with Transformations

• using the extract and summary statistics transformations
• exploring SQL transformations
• establishing status handling
• using the Data Validation transformation
• using the Transpose, Sort, Append, Rank, and List Data transformations
• using the Apply Lookup Standardization, Standardize with Definition and One-Way Frequency transformations(self-study)

Working with the Loop Transformations

• introduction to the loop transformations
• iterating a job
• iterating a transformation

Working with Slowly Changing Dimensions

• defining slowly changing dimensions
• using the SCD Type 2 Loader and Lookup transformations
• using the SCD Type 1 Loader transformations
• introducing the Change Data Capture transformations (self-study)

Creating Custom Transformations

• using the new transformation Wizard
• using the new transformation wizard

Working with the Table Loader Transformations

• exploring the basics of the Table Loader transformations
• exploring the load styles of the Table Loader transformation
• managing indexes and constraints during loading
• exploring bulk loading for DBMS tables

Working with Databases

• introduction to In-Database processing
• using In-Database processing
• exploring ELT processing
• using DBMS functions

Additional Topics for SAS Data Integration Studio Users

• overview
• analyzing metadata using impact analysis
• comparing tables
• conditional execution
• metadata promotion
• version control
• establishing checkpoints

Deploying Jobs

• introduction
• deploying jobs for scheduling
• deploying jobs in batch
• deploying jobs as stored processes

Implementing Data Quality Techniques (self-study)

• verifying data quality settings
• using the DataFlux transformation

Duration: 2 days
Cost: €1188

Overview: 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.

Pre-requisites: Before attending this course, you should have completed the SAS Programming 2: Data Manipulation Techniques course or have equivalent knowledge. Specifically, you should be able to

• use a DATA step to read from or write to a SAS data set or external file
• use DATA step programming statements such as IF-THEN/ELSE, DO WHILE, DO UNTIL, and iterative DO
• use SAS data set options such as DROP=, KEEP=, and OBS=
• use character functions such as SUBSTR, SCAN, INDEX, and UPCASE
• form subsets of data using the WHERE clause
• create and use SAS date values and constants
• use SAS procedures such as SORT, PRINT, CONTENTS, MEANS, FREQ, TABULATE, and CHART.

This course addresses Base SAS software.

This course is appropriate for students who are using SAS 9 software.

Who: Experienced SAS programmers who have a sound understanding of DATA step processing and who want to write SAS programs that are reusable and dynamic

Outline: The course will explore the following topics through a series of interactive workshop sessions:

• perform text substitution in SAS code
• automate and customize the production of SAS code
• conditionally or iteratively construct SAS code
• use macro variables and macro functions

Duration: 3 Days

Overview

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.

Presented by

Aoife has spent the last 15 years developing analytical models and processes for major national and international companies in banking, finance, insurance, gaming and manufacturing. Aoife has developed particular expertise in customer insight analytics, fraud analytics, and risk analytics.

Aoife founded The Analytics Store in 2009 to peruse her passionate belief in the importance of developing in-house analytics talent in organisations. Aoife works with organisations to help them build world class analytics teams and processes through a unique mix of training, consultancy and mentoring.

Aoife is a co-author of the textbook “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples & Case Studies” published in 2015 with MIT Press.

Learn how to

During this training course, you will learn the following:

• How to move from business questions to great data visualisations and beyond
• How to apply the fundamentals of data visualisation to create informative charts
• How to choose the right visualisation type for the job at hand
• How to use the exploration environment in SAS Visual Analytics to perform ad-hoc analysis
• How to design and develop dashboards in SAS Visual Analytics that people will love to use
• How to tell stories that from your business data to inform better business decisions

Who Should attend

This course is for anyone who analysis business data as part of their job. Some key titles include:

• Analytics Professionals
• Business Intelligence Professionals
• Reporting Specialists
• Data Scientists
• Business Analysts.

Prerequisites

To attend this course delegates should be some knowledge in the use of data analysis tools such as reporting tools, spreadsheet software or business intelligence tools. Prior knowledge of SAS is no required.

Course Outline

Day 1 Topics

From Business Questions to Data Visualisation and Beyond

The first step in any data analysis project is to move from a business question to data analysis and on to a complete solution. This section will examine this conversion emphasizing:

• The use of data visualisation to address a business need
• The need for good quality data to be available to address business problems and how to recognise and address data quality issues.
• The need for processes to be in place to take advantage of, and support, analysis efforts

Fundamentals of Data Visualisation

In this section the following topics will be covered

• Fundamentals of data visualisation
• The visual analytics process
• Data and its characteristics
• Transforming data

Introduction to SAS Visual Analytics

In this section, the main functionality of SAS Visual Analytics will be explained including:

• examining Visual Analytics Explorer
• selecting data and defining data item properties
• creating visualizations
• enhancing visualizations with analytics
• interacting with visualizations and explorations

Day 2 Topics

Key Components of Good Data Visualisation

In this section the following topics will be covered

• Colour theory
• Graphical perception & communication
• Interaction design
• Mapping visual encodings to data dimensions

The Visualisation Zoo

This section will explain the visualisation of different characteristics of data and mapping the data type to the correct type of chart. The type of data characteristics and corresponding chart types covered in this section are:

• Trends
• Comparisons
• Maps
• Networks
• Hierarchies
• Text
• KPIs

Data Exploration with SAS Visual Analytics

Exploring data to answer business questions is one of the key uses of applying good data visualisations techniques within SAS Visual Analytics. In this workshop we will apply all of the data visualisation theory within SAS Visual Analytics to uncover trends within the data to answer specific business questions.

Day 3 Topics

Design and Developing Effective Dashboards & Reports

In this section, the skills learned in the previous section will be brought together in the design and development of dashboards and reports.

• What is a dashboard and why use them
• What are the challenges of designing good dashboards
• Designing good KPI’s
• The characteristics of well-designed dashboards
• The process of in the designing good dashboards
• The best practices of dashboard design
• How we can use technology to enhance the users experience

Designing Dashboards that people will love using SAS Visual Analytics
In this section, we will implement the full process from business question to final dashboard using SAS Visual analytics

• creating a simple report
• creating data items and working with graphs
• working with filters and report sections
• establishing interactions, links, and alerts
• working with gauges and display rules
• working with tables

Duration: 3 days
Cost: €1782

Overview: 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.

Pre-requisites: Before attending this course, you should have completed the SAS Programming 2: Data Manipulation Techniques course or have equivalent knowledge. Specifically, you should be able to

• use a DATA step to read from or write to a SAS data set or external file
• use DATA step programming statements such as IF-THEN/ELSE, DO WHILE, DO UNTIL, and iterative DO
• use SAS data set options such as DROP=, KEEP=, and OBS=
• use character functions such as SUBSTR, SCAN, INDEX, and UPCASE
• form subsets of data using the WHERE clause
• create and use SAS date values and constants
• use SAS procedures such as SORT, PRINT, CONTENTS, MEANS, FREQ, TABULATE, and CHART.

This course addresses Base SAS software.

This course is appropriate for students who are using SAS 9 software.

Who: Experienced SAS programmers

Outline: The course will explore the following topics through a series of interactive workshop sessions.  You will compare various SAS programming techniques that enable you to

• benchmark computer resource usage
• control memory, I/O, and CPU resources
• create and use indexes
• combine data horizontally
• use hash and hiter DATA step component objects and arrays as lookup tables
• compress SAS data sets
• sample your SAS data sets
• create and use SAS data views
• safely reduce the length of numeric variables
• create user-defined functions and informats.

Title: SAS Enterprise Guide 2: Advanced Tasks and Querying (EG 7.1)

Duration: 2 Days

Cost: €1,400

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.

Learn how to:

• use tasks to transpose, stack, rank, and create a random sample of your data
• use functions to convert the data type from character to numeric and from numeric to character
• use conditional logic in the Query Builder to create new columns
• use multiple value prompts.

Pre-requisites: Before attending this course, students should understand how to navigate the SAS Enterprise Guide environment, create projects, add data sources, accomplish basic analysis and reporting using tasks, and create queries. You can gain this knowledge by taking the SAS Enterprise Guide 1: Querying and Reporting (EG7.1) course or by completing the Getting Started tutorial in SAS Enterprise Guide. No SAS or SQL programming experience is required.

This course uses SAS Enterprise Guide software.

Who: Non-programmers with SAS Enterprise Guide experience, as well as experienced programmers with SAS Enterprise Guide experience

Outline:

Introduction

• course overview
• course logistics

Using the Data Menu

• Append Tables task
• Split Columns task
• Stack Columns task
• Random Sample task
• Sort Data task

Using Functions in the Query Builder

• introduction to SAS functions
• manipulating numeric values
• manipulating character values
• converting data type

Prompting and Conditional Processing

• multiple values prompts
• range prompts
• conditional processing of project steps

Recoding Data

• recoding values
• recoding values based on a condition
• writing CASE expressions
• creating and applying custom formats

Grouping and Filtering

• grouping and summarizing data
• including detail and summarized data
• filtering summarized data in groups

Learning More

• SAS resources
• beyond this course

Additional Query Topics

• query options
• querying DBMS tables