Business Knowledge Series course

Presented by Jeffrey Thompson, Ph.D., Analytical Training Consultant, Education Division, SAS

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills required to succeed in today's highly analytical and data-driven economy. This course introduces the basics of data management, decision trees, logistic regression, segmentation, design of experiments, and forecasting.

This course combines scheduled, instructor-led Live Web sessions with independent activities, such as reading assignments and hands-on exercises, for a highly engaging learning experience. The course is delivered over a period of five weeks with an online orientation in week one and two or three Live Web sessions per week thereafter. Students communicate with classmates and the instructor during Live Web sessions and through online forums. To achieve maximum benefit from this course, students should allocate 8 to 12 hours per week to the following:

• participating in the two or three weekly Live Web sessions with your instructor (3.5 hours each)
• completing all weekly assignments (these can include reading assignments and hands-on exercises) before the next scheduled Live Web session (1 to 1.5 hours a week).

Upon request, this course can also be delivered as a private 5-day on-site class at your location or a SAS training center.

Learn how to

• express a business problem as a manageable analytical question
• identify the appropriate data to address the question
• select statistical analyses that help answer the question
• select a champion from a set of competing models (analyses)
• apply the results of the champion analysis to new data for scoring, forecasting, or both
• translate complex statistical results into actionable business decisions.

Who should attend

Business analysts with some experience in statistical analysis and reporting who want to make better use of their data

Formats available	Duration
Classroom:	13 days
e-Learning:	21 hours/180 day license

Before attending this course, you should have taken a college-level course in statistics, covering distribution analysis, hypothesis testing, and regression techniques or have equivalent knowledge.

This course addresses SAS Enterprise Guide, SAS Forecast Server, SAS Enterprise Miner software.

Orientation

• course orientation

Overview

• overview of business analytics
• analytical software
• data management concepts

Basics of Data Mining

• honest assessment
• methodology for predictive modeling
• rapid predictive models

Predictive Modeling

• introduction to decision trees
• introduction to logistic regression analysis
• making model comparisons
• scoring data
• managing real-world modeling problems
• continuous improvement through model management

Design of Experiments

• why use business experiments?
• multi-factor experiments
• blocking
• analyzing data from an experiment

Forecasting

• introduction to forecasting
• time series characteristics and components
• time series regression models
• time series data and hierarchical data structures
• accommodating special events in time series models
• optimizing marketing dollars across channels over time

Segmentation

• customer segmentation
• which cases are similar?
• which products are purchased together?
• what is the next product purchased?
• what do my segments tell me about my customers?