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Syllabus
Course 1: Introduction to Data Mining
Course 2: An Introduction to Database Marketing
Course 3: Exploratory Data Analysis
Course 4: Emerging Standards in Data Mining
Course 5: Data Mining Techniques for Novices
Course 6: Advanced Data Mining Techniques

Course 1: An Introduction to Data Mining
  Benefits of Attending the Course
At the end of the course the attendees will be able to:
• Appreciate the value add that Data Mining brings to the field of decision support over existing technologies lies On-line Analytical Processing (OLAP) and query and reporting tools.
• Understand the steps that must be undertaken in any data mining project
• Identify business situations that can benefit from data mining
• Plan and manage a data mining project
• Confidently discuss the role and applicability of data mining to business problems

Course Content
• What is data mining?
• A brief History of Data Mining
• Data Ming and the Decision Support Landscape
• Query and Reporting Tools
• OLAP

• The Data Mining Process
• From Business Problem to Business Solution
• Data Pitfalls
• Process Design
• Model Building
• Deployment Architectures
• The CRISP-DM Methodology

• Data Mining Goals
• Classification
• Regression
• Segmentation
• Dependency Modeling
• Association Rules
• Sequence Rules

• An Introduction to Mining Concepts
• Modeling Techniques
• Decision Tree Induction
• Neural Networks
• Lazy Learning
• K-Means Clustering

• Knowledge Validation and Performance Estimation
• Confusion Matrix
• Lift

• Case Studies
• Finance: Cross Selling
• Telecommunication: Churn Analysis
• Manufacturing: Yield Enhancement

Pre-requisites
None

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Course 2: An Introduction to Database Marketings
  Benefits of Attending the Course
At the end of the course the attendees will:
• Gain an understanding of key Database Marketing Concepts like RFM, Life Time Value and Segmentation
• Understand how databases can be used to create profitable, powerful customer communications
• Appreciate the economic value of improved targeting through the use of Predictive Modeling
• Understand the principles of designing Database Marketing experiments
• Understanding the dynamics of Customer Loyalty throughout the Customer Life Cycle: Acquisition/Retention and Cross-Sell programs
• Understand the principles of Customer Segmentation
• Appreciate a basic set of metrics for measuring Database Marketing effectiveness

Course Content
• Database Marketing Concepts
• The Customer Life Cycle
• Customer Acquisition, Retention and Cross-Selling programs
• Recency, Frequency and Monetary Value
• Life Time Value
• Response Attribution
• The importance of response deciles

• The basics of DBM financials-how these activities create value
• Minimize expenses while maximizing program effectiveness

• Database Marketing Methodology
• Experimental design
• Target/Control Groups

• Introduction to predictive modelling to support DBM
• Customer segmentation techniques
• Metrics for Database Marketing


Pre-requisites
None

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Course 3: Exploratory Data Analysis
  Benefits of Attending the Course
At the end of the course the attendees will:
• Develop data investigative skills and understand how to interpret visualizations of data
• Understand how to interpret descriptive statistics
• Appreciate the pitfalls within the data that need to be identified and dealt with prior to mining the data
• Understand the various reasons for missing data and how to handle the missing values
• Have an appreciation for the subject of data sampling and how and when it should be used
• Understand the role of data transformations on the resulting of data mining
• Appreciate the effect of the choice of mining tool on the specification of the data pre-processing

Course Content
• The Data Investigative Process
• Basic concepts in exploratory data analysis
• Using cross-tabulations to identify patterns and trends
• Correlating variables
• Data Visualisation and interpretation
• Histograms, scatter-plots, etc.
• Interpreting distributions
• Dealing with complex data structures
• Normalisation
• Data Granularity and Information Content
• Understanding the nature of data
• Descriptive Statistics-mean, median, mode, n-tiles, etc.

• Handling Noisy Data
• Best Practice on Missing Values
• Dealing with missing values
• Identifying Outliers
• Methods for dealing with Outliers

• Data Transformation
• Vectoring Variables/Pivoting e.g. lag function/time series analysis
• Range and Distribution normalisation
• De-normalising data for analysis
• Generating Summaries
• Transformation Logic-log, inverse log, root, cube, etc.
• Effect of Modelling tool
• Correlation
• Factor/principal component analysis
• Combination of variables
• Handling variable data types
• Converting categorical to numeric attributes (binning)
• Converting numeric to categorical attributes

• Sampling and Test Details
• Different Sampling methodologies
• Stratification
• Random
• Sample size selection
• Measuring the information content of data
• Validating the sample as a reflection of the underlying population
• Simple tests of significance



Pre-requisites
Basic SQL skills

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Course 4: Emerging Standards in Data Mining
  Benefits of Attending the Course
At the end of the course the attendees will:
• Gain an understanding into emerging standards for Data Mining
• Gain an understanding of Knowledge Representation and PMML
• Insights into the emerging standard Java API for Data Mining
• Gain and appreciation for Microsoft’s OLE DB/DM

Course Content
• Predictive Modelling Markup Language (PMML)
• Motivation for developing a Knowledge Representation Standard based on XML
• Description of XML elements with PMML
• Examples of Data Mining Models represented in PMML
• The Data Mining Group and the current state of PMML

• The Java Data Mining API (JDMAPI)
• Motivation for building a standard API for Data Mining
• Some Use Cases
• The concepts and associated interfaces
• Code Examples
• Related Standards

• OLE DB/DM
• XML for Analysis


Pre-requisites
Basic XML
Basic Java
Basic Object Oriented Methodologies
An Introduction to Data Mining

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Course 5: Data Mining Techniques for Novices
  Please note that if you are bringing data with you we strongly suggest that you arrange this prior to arriving for the course by sending an e-mail to: training@corporateintellect.com.


Benefits of Attending the Course
At the end of the course the attendees will:
• Gain a deep understanding of some of the most commonly used data mining techniques
• Appreciate the pros and cons of using each of the modeling techniques
• Understand the commonly used metrics for estimating the accuracy of a model
• Hands on experience of using the modeling techniques
• Understand how to interpret the outputs from the modelling

Course Content
• Modeling Techniques*
• Decision Tree Induction
• Feedforward Neural Networks
• Naïve Bayes
• Regression
• Nearest Neighbour
• K Means
• Association Rules
• Sequence Rule Discovery

• Knowledge Validation
• Mean Absolute Error/ Error Rate
• Sensitivity/Specificity and ROC curves
• Cross Validation

*Note that for each of the modeling techniques, the following topics will be covered
• How does it work?
• Common parameters available and their effect on the training of the model
• Interpretation of results
• Mean Absolute Error/ Error Rate
• Sensitivity/Specificity and ROC curves
• Cross Validation


Pre-requisites
An Introduction to Data Mining

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Course 6: Advanced Data Mining Techniques
  Please note that if you are bringing data with you we strongly suggest that you arrange this prior to arriving for the course by sending an e-mail to: training@corporateintellect.com.


Benefits of Attending the Course
At the end of the course the attendees will:
• Gain an understanding of Feature Subset Selection
• Understand how to approach the development of Hybrid Models
• Gain an understanding of advanced mining techniques


Course Content
• Feature Selection
• Wrapper and Filter methods

• Hybrid Models
• Model combinations
• Introspection
• Ensemble Models
• Bagging/Boosting

• Advanced Modelling Techniques
• Support Vector Machines
• Relational Data Mining
• Survival Analysis
• Kaplan Meier
• Cox’s Regression


Pre-requisites
An Introduction to Data Mining
Data Mining Techniques for Novices

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