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Training Courses

Are you uncertain of which training courses would be most suitable for you? To meet your varied training needs SPSS South Africa offers core and complimentary courses according to the role that you play within your organisation or as an individual.

I use SPSS for statistical analysis

The potential applications of statistical analysis are endless, but there are common procedures, methods and products that can help you in any situation. Our courses for statistical analysis cover basic and advanced statistical concepts and techniques, as well as specific procedures and general applications such as forecasting. Whether you are a statistical analysis beginner or an expert statistician, SPSS has a course to suit your needs.

Core courses

Putting Statistics into Practice

Formerly Statistics for Dummies

Duration

3 days

Target Audience

Anyone who requires further knowledge on statistical analysis

Prerequisites

None

Overview

The purpose of this course is to give you an understanding on the most commonly used statistical techniques. The aim of the course is to explain these statistical techniques by using practical examples. The theory and results will be explained with the objective that you will be able to apply what you have learnt in your working environment.

Course Content

Vital statistics about statistics
Introduction to Statistical Analysis
Describing categorical variables
Exploratory data analysis: scale data
Probability and inferencial statistics
Estimating with confidence
Putting a claim to the test (hypothesis)
Bivariate plots and correlations: Scale variables
Comparing categorical variables
Mean differences between groups: t-test
Introduction to regression
Mean difference between groups: one-factor ANOVA

Introduction to SPSS (Basics)

Duration

2 days

Target Audience

Anyone with little or no experience in using IBM® SPSS® Statistics who wishes to become an efficient and productive IBM® SPSS® Statistics user.

Prerequisites

Keyboard and mouse skills. Experience of working in the Windows environment.

Overview

The course logically guides you through the fundamentals of using IBM® SPSS® Statistics and is structured so as to provide effective training in the 4 stages of a typical data analysis process, namely data definition and input, data modification, data analysis and data presentation.

Objectives

Enter, edit and define data
Access data stored in databases and spreadsheet packages
Carry out basic data modifications
Undertake basic exploratory data analysis and interpret the results
Control the operation of IBM® SPSS® Statistics and manage your files and output

Course Content

Introducing IBM® SPSS® Statistics
Defining, enetering and editing data in IBM® SPSS® Statistics
Using the data viewer
Opening data files
Central tendency and dispersion
Summarising data
The output viewer
Modifying data values
Describing relationships between variables
Manipulating files
Improving output
Editing charts

Data Management & Manipulation (Intermediate)

Duration

2 days

Target Audience

This course is a natural follow-on to the Introduction to SPSS (Basics) and Introduction to SPSS & Statistics courses and is designed for anyone wishing to become more competent with the full range of file and data manipulation options, and generally increase their efficiency with IBM® SPSS® Statistics.

Prerequisites

You must be PC literate, have a sound working knowledge of IBM® SPSS® Statistics and be familiar with the topics covered on the Introduction to SPSS (Basics) course. You must also be familiar with variable definition, use of the data dictionary, setting up dates, generating basic exploratory statistics, using the compute and recode procedures and editing and saving output. These techniques must have been used in a recent version of IBM® SPSS® Statistics.

Overview

The course provides detailed training in the use of a wide range of file and data management techniques. The knowledge and competence gained will enable you to suitably manage your data files to achieve the desired data structures. Advice on optimising efficiency in everyday operations is provided and you will gain an understanding of the various options for operating IBM® SPSS® Statistics. Through an understanding of the command syntax, you will be able to efficiently manage and modify your data.

Objectives

Manage and manipulate numeric data, including multiple response data
Manage and manipulate dates and non-numeric data
Manipulate files so as to achieve the desired data structure

Course Content

Automating IBM® SPSS® Statistics using syntax and Production Mode
Further data transformations: Automatic Recode, Count, conditional transformations
Using Numeric Functions
Using System Variables
Computing Date, Time, and String variables
Helpful Data Management Features: Identify duplicate cases, Custom Attributes, Variable Sets
Aggregating Data
Merging Files - Adding cases
Merging Files - Adding variables
Editing Charts and Pivot Tables
Deploying SPSS results
Controlling the IBM® SPSS® Statistics environment
Appendix A: Optimal Binning using IBM® SPSS® Data Preparation

Introduction to SPSS & Statistics

Duration

3 days

Target Audience

This course targets those who wish to examine data using exploratory (including bi-variate) data interrogation techniques allowing the analyst to produce reliable results in order to draw
informed conclusions.

Prerequisites

Keyboard and mouse skills. Experience of working in the Windows environment and an understanding of basic Windows features. No prior knowledge on statistics or IBM® SPSS® Statistics is required.

Overview

The course provides an introduction to using IBM® SPSS® Statistics with particular regard to analysing quantitative information, data management and charting results. The training covers basic statistical theory and introduces many of the most popular statistical tests. The course focuses on how to use IBM® SPSS® Statistics to enhance the typical data analysis process through informed statistical analysis and appropriate data presentation.

Objectives

Open, enter, edit, define and modify your data
Understand the importance of types of data and be able to choose the appropriate techniques for summarising and testing each type of data
Perform univariate and bi-variate data analyses including hypotheses testing using IBM® SPSS® Statistics
Interpret the output and draw appropriate conclusions from the data
Produce high quality output (e.g. charts & tabulations) to report your findings and transfer this output to word processing applications
Control the operation of IBM® SPSS® Statistics and manage your files and output

Course Content

Principles of research design
Introducing IBM® SPSS® Statistics
Defining, entering and editing data in IBM® SPSS® Statistics
Using the data viewer ii: additional features
Opening data files
Central tendency & dispersion
Summarising data
The output viewer
Modifying data values
Making inferences about populations from samples
Checking the form of distributions
Analysing combinations of categorical & continuous data using t-tests
Manipulating files
Testing relationships between categorical variables
Improving output
Editing charts
Analysing combinations of continuous variables using correlations

Introduction to Statistical Analysis using SPSS

Duration

2 days

Target Audience

Anyone who has worked with IBM® SPSS® Statistics and wants to become better versed in its statistical capabilities. This course targets those with limited or no statistical background. The course is also an appropriate refresher for those whose main statistical experience was gained many years ago.

Prerequisites

General computer literacy. Completion of the courses, Introduction to SPSS (Basics) and/or Data Management & Manipulation (Intermediate) or experience with IBM® SPSS® Statistics, including familiarity with opening, defining and saving data files and manipulating and saving output. Basic statistical knowledge or at least one introductory-level course in statistics is recommended.

Overview

The focus of this course is an introduction to the statistical component of IBM® SPSS® Statistics. This is an application-oriented course and the approach is practical. You’ll take a look at several statistical techniques and discuss situations in which you would use each technique, the assumptions made by each method, how to set up the analysis using IBM® SPSS® Statistics as well as how to interpret the results. This includes a broad range of techniques for exploring and summarising data, as well as investigating and testing underlying relationships. You will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output and graphically display the results using IBM® SPSS® Statistics. This course uses IBM® SPSS® Statistics Base and IBM® SPSS® Data Preparation.

Course Content

Following an overview of the main features of IBM® SPSS® Statistics and an introduction to essential terminology, you will proceed logically through the following topics:

Introduction to statistical analysis
Principles of research design and process
Data cleaning and preparation: using the IBM® SPSS® Data Preparation™ module
Describing categorical data
Summarising continuous data
Measures of central tendency and dispersion
Checking the form of distribution
Probability and inferential statistics
Comparing categorical variables
Measures of association
Mean differences between groups: t test
Bivariate plots and correlations
Introduction to regression
Mean differences between groups: One-Factor ANOVA
Introduction to multiple regression

Complimentary courses

Advanced Statistical Analysis

Duration

3 Days

Target Audience

Anyone who has worked with IBM® SPSS® Statistics and wants to become better versed in the more advanced statistical capabilities of IBM® SPSS® Statistics. This course targets those who have a solid understanding of statistics and want to expand their knowledge of appropriate statistical procedures, how to set them up using IBM® SPSS® Statistics and how to interpret the results.

Prerequisites

General computer literacy. Completion of the courses, Introduction to SPSS (Basics) and/or Data Managment and Manipulation (Intermediate) or experience with IBM® SPSS® Statistics including familiarity with, opening, defining and saving data files, and manipulating and saving output. On-the-job statistical experience or completion of our course, Introduction to Statistical Analysis. Advanced statistical knowledge or at least two college level courses in statistics are recommended.

Overview

You’ll review many of the more advanced statistical procedures that are available in IBM® SPSS® Statistics. Discuss multivariate modelling techniques, such as discriminant analysis, logistic regression and loglinear models as well as exploratory techniques, cluster analysis and factor analysis. You will also discuss specialised survival analysis procedures, such as Cox regression and Kaplan-Meier, as well as some advanced ANOVA models. You’ll explore the situations when each may be used, the assumptions made by each method, how to set up the analysis using IBM® SPSS® Statistics, and how to interpret the results. This course uses IBM® SPSS® Statistics Base and features from the Regression and Advanced Models modules.

Course Content

Discriminant analysis
Binary logistic regression
Multinomial logistic regression
Survival analysis: Kaplan-Meier and Cox Regression
Loglinear models
Cluster analysis
Factor analysis
MANOVA: multivariate analysis of variance
Repeated measures ANOVA

Market Segmentation using SPSS Statistics

Duration

1 day

Target Audience

Anyone who has worked with IBM® SPSS® Statistics and is interested in knowing more about the appropriate procedures for market segmentation.

Prerequisites

Familiarity with IBM® SPSS® Statistics, including variable definition, opening and saving data files, generation of basic exploratory statistics. The understanding of Central Tendency, Dispersion and Hypothesis Testing (including the t-test) is an essential prerequisite.

Overview

The course focuses on the statistical techniques most often used to support market segmentation. The course emphasises the practical issues of setting up, running and interpreting the results of market segmentation analysis.

Objectives

This course will give you a sound understanding of profiling, clustering and predictive analytical techniques. In particular you will learn:

The underlying assumptions and types of data required for each technique
The similarities and differences between these techniques
When to use each technique and how to apply them using IBM® SPSS® Statistics products
How to interpret the results
How to build predictive models and apply them to new data

Course Content

Market segmentation methods
Cluster analysis for market segmentation: principles
Cluster analysis for market segmentation: practice
Factor analysis
Response-based segmentation I: Discriminant and logistic regression
Response-based segmentation II: CHAID analysis

Introduction to SPSS Decision Trees

Duration

1 day

Target Audience

This course will appeal to those wishing to find a means of targeting sub-groups of a population and who work primarily with categorical data, but who may have continuous data. Typical applications include identifying those most likely to respond positively to a mailing, those who may prove to be greater credit risk or those customers who are likely to churn.

Prerequisites

Experience of working in the IBM® SPSS® Statistics environment and a general understanding of key IBM® SPSS® Statistics features. Attendees should also have a solid understanding of basic statistical concepts (including measures of central tendency, dispersion and crosstabulation tables). Attendees should also know what data is available to them and what they will be trying to achieve using this module.

Overview

The course begins with a general introduction to the features of the IBM® SPSS® Decision Trees module and an overview of decision tree based methods. Attendees will then take a detailed look at each of the analytical methods within the module: CHAID, Exhaustive CHAID, C&RT and QUEST.

Objectives

The assumptions and concepts underlying Tree-Based Segmentation
To use all essential features of this module, as well as selected advanced options
To control the segmentation and classification criteria as well as the generation of output
To interpret the output from IBM® SPSS® Decision Trees and draw appropriate conclusions.

Course Content

Introduction to the IBM® SPSS® Decision Trees module
CHAID analysis
CHAID extensions and additional features
CRT classification trees
CRT regression trees
QUEST analysis
Recommendations, tips and shortcuts

Advanced Techniques: Regression

Duration

2 days

Target Audience

Those who want to know when to use and how to set up regression in IBM® SPSS® Statistics as well as how to interpret the results. IBM® SPSS® Statistics users who want to improve their understanding of regression techniques.

Prerequisites

Familiarity with IBM® SPSS® Statistics including: variable type and definition, entering and editing data, opening and saving data files, generating basic exploratory statistics (including frequency tables and crosstabulations) and the compute and recode procedures. Other essential prerequisites include an understanding of measures of central tendency and dispersion, inferential statistics, using interactive charts and editing and saving output.

Overview

This course examines regression techniques used to explore the relationships between interval scale variables in detail. You will develop an understanding of when to apply each technique, how to apply it and how to interpret the results. Additionally, the course will cover some preliminary data analysis steps, how to check the underlying assumptions and suggestions of how to proceed when your assumptions fail.

Objectives

By the end of the course you will have a solid understanding of the techniques listed below. In particular you will have learned:

The assumptions underlying each technique
When and why to use the techniques and how to apply them in IBM® SPSS® Statistics
How to interpret the output from these techniques and draw the appropriate conclusions
How to identify potential data issues like influential points and multicolliniarity

Course Content

Introduction to regression
Examining the rata
Simple regression: fit and assumptions
Multiple regression: fit and assumptions
Stepwise regression
Influential points and multicollinearity
Dummy variables
Interactions and polynomial regression
Nonlinear regression

Advanced Techniques: ANOVA

Duration

2 days

Target Audience

IBM® SPSS® Statistics users who want to improve their understanding of analysis of variance techniques. Those who want to know how to set up analysis of variance in IBM® SPSS® Statistics as well as how to interpret the results.

Prerequisites

Overview

The course focuses on the different Analysis of Variance techniques which allow you to test whether the means of several populations are the same. After discussing the basic assumptions for each technique you will check the assumptions, run the analysis and draw conclusions from the data.

Objectives

Understand the methods and assumptions behind different ANOVA models
Simple 1-way ANOVA
MANOVA
Repeated Measures

Course Content

Introduction to ANOVA
Examining data and testing assumptions
One-factor ANOVA
Multi-way univariate ANOVA
Multivariate analysis of variance
Within-subject designs: repeated measures
Between and within-subject ANOVA
Mixed Models ANOVA
Analysis of covariance
Special topics

Time Series Analysis & Forecasting

Duration

3 days

Target Audience

Anyone with time series data that requires modelling and anyone wishing to gain a sound understanding of when, why and how to build time series models.

Prerequisites

You must be PC literate. Attendees must be familiar with IBM® SPSS® Statistics, including entering and editing data, generating basic exploratory statistics and the compute and recode procedures. An understanding of measures of central tendency, dispersion and hypothesis testing is essential. It would be helpful to have a basic understanding of regression analysis.

Overview

After introducing the fundamental concepts of time-series analysis, the course focuses on a range of commonly applied analysis techniques, including curve-fitting, exponential smoothing and ARIMA modelling. Seasonal and non-seasonal data is modelled as well as looking at the added issues of modelling intervention effects.

Objectives

Define the time series data in preparation for analysis
Apply “Pure” time series models
Apply “Casual” time series models
Apply ARIMA modelling
Incorporate seasonality into your modelling process
Model interventions into your time series analysis

Course Content

The basics of time series analysis
Starting time series analysis
Smoothing time series data
Looking for outliers
Automatic forecasting with time series modeler
Measuring model performance
Fitting a simple curve to time series data
Time series regression
Exponential smoothing models
Auto Regressive Integrated Moving Average (ARIMA) models
Applying time series models
Seasonal decomposition
Modelling seasonality
Outliers detection
Intervention analysis
Transfer functions
Managing analysis documents for other analysts

Introduction to Amos

Duration

2 days

Target Audience

Individuals with an interest in Structural Equation Modelling (SEM). AMOS provides SEM techniques in a user-friendly package.

Prerequisites

Statisticians and applied quantitative researchers with some experience in multiple regression or factor analysis are encouraged to attend.

Overview

Graphical, interactive path modelling with the AMOS program is employed throughout the session. Modern advances in structural modelling and statistical methods are emphasised and demonstrated by practical research examples from different areas of the social sciences.

Course Content

Structural equation modelling
Getting started: regression with AMOS
Test model adequacy
Principles of testing applied
The general model
Special topics
Appendix a: AMOS toolbar
Appendix b: Glossary of terms

I use SPSS for automation

Our courses provide you with the knowledge to utilise the many levels of automation available within SPSS, extending the functionality and efficiency of business processes. Whether you wish to automate data management tasks, reporting processes, or any other task, there is a course for your requirements. We provide a range of courses suitable to any skill level, whether you are a complete novice or a development programmer.

Core courses

Introduction to SPSS (Basics)

Duration

2 days

Target Audience

Anyone with little or no experience in using IBM® SPSS® Statistics who wishes to become an efficient and productive IBM® SPSS® Statistics user.

Prerequisites

Keyboard and mouse skills. Experience of working in the Windows environment.

Overview

Objectives

Enter, edit and define data
Access data stored in databases and spreadsheet packages
Carry out basic data modifications
Undertake basic exploratory data analysis and interpret the results
Control the operation of IBM® SPSS® Statistics and manage your files and output

Course Content

Introducing IBM® SPSS® Statistics
Defining, entering and editing data in IBM® SPSS® Statistics
Using the data viewer
Opening data files
Central tendency and dispersion
Summarising data
The output viewer
Modifying data values
Describing relationships between variables
Manipulating files
Improving output
Editing charts

Data Management & Manipulation (Intermediate)

Duration

2 days

Target Audience

This course is a natural follow-on to the Introduction to SPSS (Basics) and Introduction to SPSS & Statistics courses and is designed for anyone wishing to become more competent with the full range of file and data manipulation options, and generally increase their efficiency with IBM® SPSS® Statistics.

Prerequisites

You must be computer literate, have a sound working knowledge of IBM® SPSS® Statistics and be familiar with the topics covered on the Introduction to SPSS (Basics) course. You must also be familiar with variable definition, use of the data dictionary, setting up dates, generating basic exploratory statistics, using the compute and recode procedures and editing and saving output. These techniques must have been used in a recent version of IBM® SPSS® Statistics.

Overview

Objectives

Manage and manipulate numeric data, including multiple response data
Manage and manipulate dates and non-numeric data
Manipulate files so as to achieve the desired data structure

Course Content

Automating IBM® SPSS® Statistics using syntax and Production Mode
Further data transformations: Automatic Recode, Count, conditional transformations
Using Numeric Functions
Using System Variables
Computing Date, Time, and String variables
Helpful Data Management Features: Identify duplicate cases, Custom Attributes, Variable Sets
Aggregating Data
Merging Files - Adding cases
Merging Files - Adding variables
Editing Charts and Pivot Tables
Deploying SPSS results
Controlling the IBM® SPSS® Statistics environment
Appendix A: Optimal Binning using IBM® SPSS® Data Preparation

Introduction to Statistical Analysis using SPSS

Duration

2 days

Target Audience

Prerequisites

General computer literacy. Completion of the courses, Introduction to SPSS (Basics) and/or Data Management and Manipulation (Intermediate) or experience with IBM® SPSS® Statistics, including familiarity with opening, defining and saving data files and manipulating and saving output. Basic statistical knowledge or at least one introductory-level course in statistics is recommended.

Overview

Course Content

Following an overview of the main features of IBM® SPSS® Statistics and an introduction to essential terminology, you will proceed logically through the following topics:

Introduction to statistical analysis
Principles of research design and process
Data cleaning and preparation: using the IBM® SPSS® Data Preparation™ module
Describing categorical data
Summarising continuous data
Measures of central tendency and dispersion
Checking the form of distribution
Probability and inferential statistics
Comparing categorical variables
Measures of association
Mean differences between groups: t test
Bivariate plots and correlations
Introduction to regression
Mean differences between groups: One-Factor ANOVA
Introduction to multiple regression

Syntax for Beginners

Duration

1 day

Target Audience

This course targets those who wish to gain an understanding of the basic structure of Syntax.

Prerequisites

Experience of working in the Windows environment and a general understanding of key Windows features. Attendees should also have basic familiarity with IBM® SPSS® Statistics procedures including variable definition, entering and editing data, opening and saving data files, compute and recode procedures, dealing with output and saving output. The beforementioned techniques must have been used in the current version of IBM® SPSS® Statistics.

Overview

The course provides users of IBM® SPSS® Statistics with the essential skills and knowledge required to become effective and productive users of Syntax.

Objectives

Open up IBM® SPSS® Statistics files using syntax
Define variables using syntax
Create Compute and Autorecode procedures
Create simple and multiple IF and DO-IF statements.

Course Content

Introduction to syntax
Opening and saving a IBM® SPSS® Statistics data file
The structure and definition of variables
Variable labels and value labels
Using the compute command
Other useful commands
The ‘if’ command
Do if…else if…end if
Related areas

Syntax for Experts

Duration

1 day

Target Audience

This course is a natural follow-on to the Syntax for Beginners course and is designed for experienced IBM® SPSS® Statistics users wishing to become proficient syntax programmers.

Prerequisites

Experience of working in the Windows environment and a general understanding of key Windows features. Attendees should also have a good working knowledge of IBM® SPSS® Statistics and Syntax used in the current version of IBM® SPSS® Statistics.

Overview

The first part of the course provides users of Syntax with an overview of fundamental programming concepts and their practical application to perform complex data manipulations. The second part of the course introduces the MACRO facility and shows how macros can help you to automate tasks to make your work more efficient and easier.

Objectives

Open complex data files using syntax
Manipulate data efficiently within syntax
Work with LOOPS and VECTORS
Understand and work with MACROS.
Course content
Introduction and syntax review
Basic IBM® SPSS® Statistics programming concepts
Practical applications for advanced syntax
Introduction to macros
Advanced macros
Output Management System

I use SPSS for data mining

In-depth customer or constituent understanding is critical to business success. Our data mining courses provide the conceptual and practical knowledge you need to accurately track and predict customer behaviour. From the fundamentals of analytical customer relationship management to using PASW Modeler for customer analysis, you will find topics that can help you develop plans for maximising customer value.

Core courses

Introduction to Modeler and Data Mining

Duration

3 days

Target Audience

Anyone with little or no experience using IBM® SPSS® Modeler. May also be unfamiliar with data mining in general.

Prerequisites

General computer literacy. It would be helpful if you had an understanding of your organisation’s data, as well as any of your organisation’s business issues that are relevant to the use of data mining. No statistical background is necessary.

Overview

This course provides you with an overview of data mining and the fundamentals of using IBM® SPSS® Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. You’ll follow the stages of a typical data mining project, from reading data, to data exploration, data transformation, modelling and effective interpretation of results. You’ll also learn how to read, explore and manipulate data with IBM® SPSS® Modeler and then create and use successful models.

Course Content

Introduction to data mining
The basics of using IBM® SPSS® Modeler
Reading data files
Data understanding
Outliers and anomolous data
Introduction to data manipulation
Looking for relationships in data
Combining data files by appending and/or merging
Aggregating data
Selecting, sampling and partitioning records
Modelling techniques in IBM® SPSS® Modeler
Rule induction
Automating modelling for binary outcomes
Automating modelling for numeric outcomes
Model understanding
Comparing and combining models
Deploying and using models
Modeler options and stream properties
Running SPSS commands from IBM® SPSS® Modeler
Data mining references

Preparing Data for Data Mining

Duration

1 day

Target Audience

This follow-up course to Introduction to Modeler and Data Mining is designed for anyone who wishes to become familiar with the full range of techniques available in IBM® SPSS® Modeler for data and file manipulation.

Prerequisites

General computer literacy. Some experience with using IBM® SPSS® Modeler, including familiarity with the IBM® SPSS® Modeler environment, creating streams, reading in data files, and doing simple data exploration and manipulation. Prior completion of Introduction to Modeler and Data Mining is strongly encouraged.

Overview

In this course, you’ll examine additional topics to aid in the preparation of data for a successful data mining project. You’ll learn how to partition records from files, handle missing data, modify fields and create new fields as well as work with strings and sequence data.

Course Content

Data preparation
Sampling data
Working with dates
Working with string data
Working with sequence data
Data transformations on categorical fields
Data transformations on continuous fields
Exporting data files
Efficiency with IBM® SPSS® Modeler

Complimentary courses

Predictive Modelling with Modeler

Duration

3 days

Target Audience

This course follows either Introduction to Modeler and Data Mining or Preparing Data for Data Mining and is essential for anyone who wishes to become familiar with the full range of modelling techniques available in IBM® SPSS® Modeler to create predictive models.

Prerequisites

General computer literacy. Experience using IBM® SPSS® Modeler, including familiarity with the IBM® SPSS® Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the type and data audit nodes), basic data manipulation (including the derive and select nodes) and creation of models. Prior completion of Introduction to Modeler and Data Mining is required and completion of Preparing Data for Data Mining is strongly encouraged. An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modelling techniques.

Overview

This course shows you how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression and the binary classifier node. Feature selection and detection of outliers are also discussed. Expert options for each modelling node are discussed in detail and advice is provided on when and how to use each model. You will also learn how to combine two or more models to improve prediction.

Course Content

Introduction to essential terminology
Data reduction with principal components
Preparing data for modelling
Decision trees
Neural networks
Linear regression
Time series analysis
Logistic regression
Discriminant analysis
Finding the best model for binary outcomes
Finding the best model for numeric outcomes
Getting the most from models
Searching for data anomalies
Selecting predictors
Binary Classifier node
Combining models to improve performance
Decision list

Clustering and Association Models with Modeler

Duration

1 day

Target Audience

This course follows Introduction to Modeler and Data Mining or Preparing Data for Data Mining and is designed for anyone who wishes to become familiar with the full range of modelling techniques available in IBM® SPSS® Modeler to segment (cluster) data and to create models with association or sequence data. If you want to successfully build such models using IBM® SPSS® Modeler, this course is an essential part of the learning

Prerequisites

General computer literacy. Experience using IBM® SPSS® Modeler, including familiarity with the IBM® SPSS® Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the type and data audit nodes), basic data manipulation (including the derive and select nodes) and creation of models. Prior completion of Introduction to Modeler and Data Mining is required and completion of Preparing Data for Data Mining is strongly encouraged. An introductory course in statistics or equivalent experience would be helpful for the statistics-based modelling techniques.

Overview

In this course you’ll learn how to segment or cluster data with all the clustering techniques available in IBM® SPSS® Modeler. You’ll also discover how to create association models to find rules describing the relationships among a set of items and create sequence models to find rules describing the relationships over time among a set of items.

Course Content

Introduction to models for clustering and association
Techniques for clustering
Association rules
Advanced association rules
Sequence detection
Advanced sequence detection
Additional readings

Introduction to SPSS Text Analytics

Duration

2 days

Target Audience

This course follows Introduction to Modeler and Data Mining and is designed for anyone who wishes to become familiar with the text mining capability of IBM® SPSS® Modeler. For people wishing to successfully build such models using IBM® SPSS® Modeler, this course is an essential part of the learning process.

Prerequisites

Overview

This two-day course shows how you can convert text to data for use in text mining and data mining applications. You’ll review the basic concepts of text analysis and learn how to extract and refine concepts from text, convert these concepts to data, and then perform text mining and data mining analyses. Both automation and deployment are discussed.

Course Content

Introduction to essential terminology
Introduction to text mining
IBM® SPSS® Text Analytics
Extracting text in a field
The generated model
Analysis for concepts
Expert extraction options
Extracting text in documents
Text Mining Builder™
Scoring new data
Linguistics and text mining

I use SPSS for market research

Once you have conducted your survey, you need to turn the data into actionable results. Our ranges of courses enable you to use either SPSS’ advanced statistical and graphing capabilities or reporting and tabulation capabilities from the Data Collection Family to analyse, interpret and present your results. As a result you can make smarter decisions more quickly by uncovering key facts, patterns and trends.

Core Courses

Introduction to SPSS (Basics)

Duration

2 days

Target Audience

Anyone with little or no experience in using IBM® SPSS® Statistics who wishes to become an efficient and productive IBM® SPSS® Statistics user.

Prerequisites

Keyboard and mouse skills. Experience of working in the IBM® SPSS® environment.

Overview

Objectives

Enter, edit and define data
Access data stored in databases and spreadsheet packages
Carry out basic data modifications
Undertake basic exploratory data analysis and interpret the results
Control the operation of IBM® SPSS® Statistics and manage your files and output

Course Content

Introducing IBM® SPSS® Statistics
Defining, entering and editing data in IBM® SPSS® Statistics
Using the data viewer
Opening data files
Central tendency and dispersion
Summarising data
The output viewer
Modifying data values
Describing relationships between variables
Manipulating files
Improving output
Editing charts

Data Management & Manipulation (Intermediate)

Duration

2 days

Target Audience

Prerequisites

You must be PC literate, have a sound working knowledge of IBM® SPSS® Statistics and be familiar with the topics covered on the Introduction to SPSS (Basics) course. You must also be familiar with variable definition, use of the data dictionary, setting up dates, generating basic exploratory statistics, using the compute and recode procedures and editing and saving output. These techniques must have been used in a recent version of IBM® SPSS® Statistics.

Overview

Objectives

Manage and manipulate numeric data, including multiple response data
Manage and manipulate dates and non-numeric data
Manipulate files so as to achieve the desired data structure

Course Content

Automating IBM® SPSS® Statistics using syntax and Production Mode
Further data transformations: Automatic Recode, Count, conditional transformations
Using Numeric Functions
Using System Variables
Computing Date, Time, and String variables
Helpful Data Management Features: Identify duplicate cases, Custom Attributes, Variable Sets
Aggregating Data
Merging Files - Adding cases
Merging Files - Adding variables
Editing Charts and Pivot Tables
Deploying SPSS results
Controlling the IBM® SPSS® Statistics environment
Appendix A: Optimal Binning using IBM® SPSS® Data Preparation

Introduction to Statistical Analysis using SPSS

Duration

2 days

Target Audience

Prerequisites

Overview

Course Content

Following an overview of the main features of IBM® SPSS® Statistics and an introduction to essential terminology, you will proceed logically through the following topics:

Introduction to statistical analysis
Principles of research design and process
Data cleaning and preparation: using the IBM® SPSS® Data Preparation™ module
Describing categorical data
Summarising continuous data
Measures of central tendency and dispersion
Checking the form of distribution
Probability and inferential statistics
Comparing categorical variables
Measures of association
Mean differences between groups: t test
Bivariate plots and correlations
Introduction to regression
Mean differences between groups: One-Factor ANOVA
Introduction to multiple regression

Presenting Data with SPSS Custom Tables: Introduction

Duration

1 day

Target Audience

Anyone wanting to have greater control over the creation, formatting and layout of tables by using the custom tables module to produce flexible, high quality, high impact tables.

Prerequisites

You must be PC literate and you must be familiar with the basics of operating IBM® SPSS® Statistics. This includes defining, entering and editing data, opening and saving data files, generating basic exploratory statistics (including frequency tables, basic crosstabulations and tables of means) and editing and saving output. You must also understand the difference between categorical and scale data types. These techniques must have been used in a recent version of IBM® SPSS® Statistics.

Overview

The course concentrates on utilising the power and flexibility of the IBM® SPSS® Custom Tables module, starting with the production of simple tables and advancing on a step-by-step basis to more complex designs. You will gain an understanding of the full range of Tables options including stacking, nesting and layering variables in both rows and columns. In addition, you will learn how to combine scale and categorical variables into complex, nested combinations by using simple drag and drop procedures. The first part of the course concentrates on the fundamental operations before progressing into more advanced topics like subtotaling and custom totals that will give you a complete understanding of the capabilities of the IBM® SPSS® Custom Tables module.

Objectives

Present combinations of variables in a table using nesting and stacking
Simplify complex tables by using layering
Control the statistics and totals that are displayed in the table
Create and control subtotals
Define ordering and category exclusions within a table
Control all aspects of the table layout and printing

Course Content

Getting started with IBM® SPSS® Custom Tables
Simple tables for categorical variables
Stacking and nesting with categorical variables
Totals and subtotals for categorical variables
Tables for variables with shared categories
Summary statistics
Summarising scale variables
Multiple response sets
Formatting and editing tables
Test statistics

Presenting Data with SPSS Custom Tables: Advanced

Duration

1 day

Target Audience

Anyone who has worked with IBM® SPSS® Custom Tables and would like to use its capabilities in full. Survey and market researchers, analysts and academics who need to publish or present the results of their analysis.

Prerequisite

On-the-job experience with IBM® SPSS® Statistics and IBM® SPSS® Custom Tables or completion of the course Presenting Data with Custom Tables: Introduction.

Overview

You will learn how to use the IBM® SPSS® Custom Tables module to build more complex and customised tables and produce them more efficiently. You will see options for handling missing values, formatting and editing tables and moving tables to other software. You will be introduced to IBM® SPSS® Custom Tables syntax for recurring analyses and learn how to use IBM® SPSS® Custom Tables with other IBM® SPSS® Statistics features to produce special purpose tables.

Course Content

Formatting and editing tables
Handling missing values
Moving tables to other software, distributing tables
Introduction to IBM® SPSS® Custom Tables Syntax
Using syntax for recurring analyses
Special Purpose Tables I: Other Groupings
Special Purpose Tables II: Advanced Tables
Additional tips

Complimentary courses

Introduction to SPSS Complex Samples

Duration

1 day

Target Audience

This course is designed for survey researchers who conduct studies that involve complex samples.

Prerequisites

On the job experience with IBM® SPSS® Statistics for or completion of the Introduction to SPSS (Basics) course. Basic knowledge of statistics and sampling methodology will be helpful.

Overview

Do you conduct studies that involve complex samples? In this 1 day course learn the concepts and issues relevant to complex sampling and how to use the IBM® SPSS® Complex Samples add-on module to create a sampling design, select probability-based samples and produce statistical summaries adjusted for the sampling plan.

Objectives

The key survey methodologies, including clustering, stratified and multistage sampling
To use the Sampling Wizard to guide you through the process of designing a scheme and drawing a sample
To use the Analysis Preparation Wizard to help you prepare public-use datasets that include complex sample designs, for analysis
To correctly analyse datasets drawn using a complex sample design
To share analysis and sampling plans with your colleagues

Course Content

Sampling basics
Probability and non-probability samples
Sampling error
A sample session with IBM® SPSS® Complex Samples
Setting up a sampling plan
Stratification and clustering
One and two stage sampling designs
Data format and entry

SPSS Text Analytics for Surveys

Duration

2 days

Target Audience

Survey analysts who work with surveys containing open-ended questions.

Prerequisites

Some practical experience with survey research or analysis is helpful, but not required.

Overview

This two day course shows you how to analyse text or open ended survey questions using IBM® SPSS® Text Analysis for Surveys. You will see the steps involved in working with text data, from reading the text data to exporting the final categories for additional analysis. Topics include how to automatically and manually create and modify categories, and how to edit synonym, type, and exclude dictionaries.

Objectives

Understand the concept and theory behind text analysis
Use the essential features of IBM® SPSS® Text Analysis for Surveys
Use IBM® SPSS® Text Analysis for Surveys to import survey data in numerous formats
Reliably extract and classify key concepts within open-ended survey responses, and other free-form textual data
Transform qualitative survey responses into quantitative data, and export it to your analytical tools such as IBM® SPSS® Statistics Base for further analysis and graphing

Course Content

Introduction and overview of IBM® SPSS® Text Analysis for Surveys
Considerations before performing text analysis
Projects and help
Data access
Extracting terms
Category creation (automatic and manual)
Exporting categories
Editing dictionaries
Managing libraries and projects

Survey Analysis using SPSS

Duration

2 days

Target Audience

IBM® SPSS® Statistics users who would like an introduction to the analyses and reporting that can be performed on their survey data.

Prerequisites

General computer literacy. Completion of the Introduction to SPSS (Basics) and/or Data Management & Manipualtion (Intermediate) courses or experience with IBM® SPSS® Statistics, including familiarity with opening, defining and saving data files and manipulating and saving output. Basic statistical knowledge or at least one introductory level course in statistics is recommended.

Overview

In this two-day course you’ll review the most commonly used methods to analyse survey data, beginning with simple methods, such as crosstabulations and moving toward the advanced, such as logistic regression. You’ll discuss appropriate methods of analysis for both categorical and continuous data, as well as qualitative data analysis and the reporting and presentation of survey results. You’ll use IBM® SPSS® Statistics Base as well as features from the IBM® SPSS® Regression, Missing Values, Categories, Custom Tables and Decision Trees modules.

Course Content

Following a discussion of the logic of survey analysis, the following topics are presented with hands-on exercises.

Measurement and error: reliability and validity
Constructing scales and indices: factor analysis
Relationships between categorical variables: crosstabulation
Relationships between categorical and interval variables: t test, simple ANOVA
Analysing interval variables: correlations and scatterplots
Reporting results: custom tables and specialised charts
Clustering respondents: cluster analysis
Multivariate analysis with regression: linear regression and logistic regression
Special problems with survey data: missing data
Using decision trees to analyse categorical data

Market Segmentation using SPSS Statistics

Duration

1 day

Target Audience

Anyone who has worked with IBM® SPSS® Statistics and is interested in knowing more about the appropriate procedures for market segmentation.

Prerequisites

Overview

Objectives

This course will give you a sound understanding of profiling, clustering and predictive analytical techniques. In particular you will learn:

The underlying assumptions and types of data required for each technique
The similarities and differences between these techniques
When to use each technique and how to apply them using IBM® SPSS® Statistics products
How to interpret the results
How to build predictive models and apply them to new data

Course Content

Market segmentation methods
Cluster analysis for market segmentation: principles
Cluster analysis for market segmentation: practice
Factor analysis
Response-based segmentation I: Discriminant and logistic regression
Response-based segmentation II: CHAID analysis

Introduction to SPSS Decision Trees

Duration

1 day

Target Audience

Prerequisites

Overview

Objectives

The assumptions and concepts underlying Tree-Based Segmentation
To use all essential features of this module, as well as selected advanced options
To control the segmentation and classification criteria as well as the generation of output
To interpret the output from IBM® SPSS® Decision Trees and draw appropriate conclusions.

Course Content

Introduction to the IBM® SPSS® Decision Trees module
CHAID analysis
CHAID extensions and additional features
CRT classification trees
CRT regression trees
QUEST analysis
Recommendations, tips and shortcuts