Statistics & Modeling Courses
Numerous courses in statistics and predictive modeling. Available via...
- Home Study Course: 7 Steps to Effective Predictive Modeling Registration Information
- Live training in partnership with SAS… register here
- Custom on-site training… please contact Olivia
- BI 101: Introduction to Statistics - Details
- BI 201: 7 Steps to Effective Predictive Modeling - Details
- BI 301: Predictive Modeling Demystified - An Overview for Managers & Marketers - Details
- BI 401: Business Intelligence Success Factors: An Overview of Key Skills Needed to Succeed in the Complex, Continuously Changing World of Business Intelligence - Details
This course is designed to introduce basic statistical concepts that are commonly used in business. Using standard business examples, the concepts are clearly explained in non-technical language.
This course can be taught in a lecture format (1 day) or a hands-on format using SAS® examples in Base SAS and SAS/STAT®, SAS Enterprise Guide® or SAS Enterprise Miner® (2 day).
1. Why are statistics important in business?
To stay competitive, businesses are increasingly using statistics to improve their marketing, risk and retention efforts as well as their business processes. As more data become available, the pressure to effectively discover patterns, measure changes and generate knowledge is accelerating.
2. What is probability theory and how does it relate to statistics?
Statistics is based on probability theory or the laws of chance. A basic introduction to probability theory helps us understand the power and usefulness of statistical methods.
3. Summary Statistics
Simple measures of frequency, central tendency (mean, median, mode) and spread (standard deviation, variance, range, skewness) are powerful tools for understanding the distributions of large data sets.
4. Important Distributions
The normal and binomial distributions are commonly used for understanding business trends and predicting future activities. Companies are leveraging the power of these distributions to increase their marketing and processing efficiencies.
5. Populations and Samples
Samples are commonly used to estimate population values. Sample allows the researcher to draw conclusions about the population based on sample statistics.
6. Hypothesis Testing and Confidence Intervals
Tools for testing the significance of a prediction and developing boundaries that are correlated with levels of certainty are useful for planning and forecasting.
7. Analysis of Variance
If two or more groups of interest (customers, patients, crops) are treated differently, an Analysis of Variance Table (ANOVA) displays the variability between the groups which allows you to determine if difference in treatment or action is significant.
8. Clustering
A technique for assigning people or items of interest to groups based on their similarity, clustering identifies the dominant characteristics of distinct groups within a population.
9. Correlation and Regression
Correlation and linear regression measure the strength and direction of the relationship between two more or more independent continuous variables and a continuous dependent variable. Logistic regression measures the strength and direction of the relationship between one or more continuous independent variables and a discrete dependent variable.
Prerequisites
Lecture: None
Hands-on: SAS/STAT® course requires some familiarity with basic SAS programming. No prerequisites are necessary for SAS Enterprise Guide® or SAS Enterprise Miner®.
Who Should Attend
Data Analysts
Marketers
Programmers
Managers
Duration: Lecture – 1 day; Hands-on – 2 days
Contact Olivia for pricing
This course is designed to teach statisticians, analysts and modelers the basics of predictive modeling in a data-rich, fast-paced business environment. The course covers how to successfully design, build, validate and implement predictive models for a variety of applications and industries. With a focus on the business goals, the course relates each step to the company’s specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.
Steps 3 through 7 contain proprietary SAS® macros and applications of the powerful Output Delivery System to streamline data cleansing, variable preparation, model development, validation and implementation. Data is provided for a hands-on experience using Base SAS, SAS/STAT, SAS Graph and MSExcel®.
Chapter 1: Defining the Objective
(The first step in any modeling process is defining the objective. This chapter explores ways to think about defining the model objective function in relation to the business goals as well as the overall company strategy. Several methods for developing models are discussed including linear regression, logistic regression and classification trees.
Numerous types of models are explained including response, activation, risk, retention, and lifetime value. The main case study, developing an activation model for life insurance, is introduced.
Chapter 2: Gathering the Data
Accurate, actionable, accessible data is the lifeblood of any successful model. This chapter discusses various types of data as well as its many sources, both internal and external. Numerous examples are provided for ways to collect and/or generate valid samples for model development. Multiple scenarios over a variety of industries describe data for acquisition, retention, risk, cross-sell and up-sell. Sampling theory and techniques are discussed.
Chapter 3: Preparing the Data for Modeling
The average modeler spends 60% of his or her time preparing data. This chapter details the entire data preparation process beginning with a description of the different classifications of data and how they can be adapted for predictive modeling. Several techniques are introduced for handling common data problems such as missing values and outliers.
Chapter 4: Selecting and Transforming the Variables
Determining the best fit is essential to good model performance. The underlying structure of the independent variables in relation to the dependent variable, determines the power and longevity of a model. This chapter details the steps for binning and transforming independent variables to insure the best fit with the dependent variable.
Special consideration is given to the fact that marketing data can have hundreds or even thousands of variables. This chapter introduces several quick methods for identifying the best candidate variables. Programs are introduced that automatically segment and transform the most powerful variables, to insure the best fit. Finally, selection methods are combined to easily bring the best fitting variables into the final stage of the modeling process.
Chapter 5: Processing and Evaluating the Model
All the preparation work up to this point makes this next step run smoothly. This chapter introduces several methods for processing and evaluating the model, with a practical discussion on the ideal number of variables. Weights of Evidence and Information Values are calculated. For our main case study, we use various options within PROC LOGISTIC to determine the model with the best fit. SAS®’s output delivery system (ODS) is used to capture information and display the data. Several models are compared using KS, Gini, C-Statistic, Bayes Information Criterion (BIC), decile analysis and SAS Graph. Validation data are scored, tabulated and compared using both SAS® & MSExcel®.
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Chapter 6: Validating the Model
By definition, models should perform well on the development data. Plus, if the hold-out sample is randomly selected, the model performance should score the validation data with similar results. A true test of model performance is how well it performs on data from a different time period or market area. This chapter demonstrates three powerful methods for insuring model fit. 1) Scoring alternate data is the best way to tell if your model will perform in a real campaign; 2) Bootstrapping uses simple resampling techniques to find confidence intervals around your estimates; 3) Key Variable Analysis calculates important market factors as they are effected by the model, thus insuring reasonable results.
Chapter 7: Implementing and Maintaining the Model
Effective implementation is a combination of business intelligence and well designed procedures. This chapter begins with scoring a new data set with the new model. Several auditing procedures are discussed. Tracking and model maintenance are emphasized as best practices.
After attending this course, you will be able to quickly determine what model or combination of models will do the best job of meeting your company’s objectives. You will have all the steps to successfully develop, validate and implement your predictive model.
Prerequisites:
Familiarity with SAS® including data step
Basic knowledge of statistics
Who Should Attend
Analysts
Modelers
Statisticians
Duration: 2 day
Contact Olivia for pricing
This course offers a non-technical tour of descriptive and predictive modeling methods including segmentation, clustering, linear and logistic regression, neural networks, genetic algorithms, classification trees (often referred to as decision trees). Using numerous database marketing examples for acquisition, risk, retention and profitability, applications of each method is discussed, evaluated and compared for its power, stability and longevity.
Prerequisites:
None
Who Should Attend
Executives
Managers
Marketers
Duration: 1 day
Contact Olivia for pricing
Data mining and business intelligence offer incredible opportunities for companies to improve their bottom lines. However, increased complexity and the continual introduction of new technologies as well as shifting markets and consumer demands require companies to master a broad set of skills. Skilled management has never been more important. Studies show that roughly 70% of business intelligence projects fail due to poor leadership. This presentation will discuss concepts and provide insights into some very basic skills that are necessary for effective leadership in a high-tech, data-driven industry. A few of the topics are described below.
Communication is an essential skill in the complex world of business intelligence. As you turn to high tech solutions for your database marketing challenges, your employees require highly specialized and varied skill sets. In many organizations, each group speaks a “different language.” Yet their ability to effectively communicate is critical to your business success.
Non-linear thinking is quickly becoming vital for maintaining a competitive advantage. Business intelligence solutions are linear processes that can be automated. It’s just a matter of time before these processes are available to your competitors. You must become proficient at ‘whole brain’ thinking to creatively leverage these linear tools and insure continued innovation.
Managing change requires vision and leadership. To leverage opportunities that are on the horizon, you must thrive on uncertainty, inspire your employees, trust you instincts and become an intelligent risk taker. The rewards await those leaders who set clear goals, move forward with integrity and trust the process.
Prerequisites:
None
Who Should Attend
Executives
Managers
Strategists
Duration: 1 day
Contact Olivia for pricing
