2015 Workshops for Professors

Transcription

1 SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1

2 Workshops for Professors As the market demand for professionals with data management, analytical and problem-solving skills increases, SAS seeks to provide university students with an analytical toolset that enables them to address modern, data-intensive business problems. To accomplish this goal, SAS is holding workshops for professors who are bringing the power of analytics to the classroom at no charge. The courses include course materials, continental breakfast and lunch. Travel and accommodations are the responsibility of the attendee. These offerings are by invitation only. If you would like to be included on the invitation list for this training, please your request to Teaching materials for these workshops are available at no cost for self-study or use in your classroom even if you are unable to attend the workshop. These course packages include PowerPoint slides, instructor notes and data sets. To receive these materials, please send requests to and reference training material in the subject line. The topics covered in these workshops are designed to be taught at both the undergraduate and graduate level. The courses are intended to encompass one semester of teaching, equivalent to four credit hours per semester. This consists of three hours of lecture per week plus a weekly lab. 2 Learn more at: sas.com/teach [email protected]

3 2015 Workshop Schedule San Diego Jan Using SAS to Clean Big Data Jan Advanced Predictive Modeling Using SAS Enterprise Miner 13.1 Jan Jan. 15 Jan. 16 June 8-9 June 8-12 June 10 June 11 Applied Mixed Models SAS Visual Analytics for Professors Introduction to SAS Visual Statistics Text Analytics Using SAS Text Miner Advanced Business Analytics SAS Visual Analytics for Professors Introduction to SAS Visual Statistics Cary, NC SAS Headquarters July 13 Data Manipulation and Analytics Using SAS Enterprise Guide July July July 15 July 16 July Text Analytics Using SAS Text Miner Applied Analytics Using SAS Enterprise Miner SAS Visual Analytics for Professors Introduction to SAS Visual Statistics Advanced Business Analytics Classes are offered as a series of days so that you may take several classes. Two locations are also offered with several classes repeated at both locations. 3

4 Advanced Business Analytics Advanced Business Analytics is an academic course designed to be taught on the undergraduate or graduate level during a 15-week semester. The course consists of three hours of lecture per week plus a weekly lab. The course features corporate case studies and hands-on exercises to demonstrate the concepts that are presented. Advanced Business Analytics uses software that is offered at no cost through the SAS OnDemand for Academics cloud-based software-access program. Express a business problem as a manageable analytical question. Identify the appropriate data to address the question. Select analytical analyses that help you 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 analytical results into business decisions. Prerequisites: Students are most successful when they have completed one semester in statistics, including correlation, regression and distribution analysis. Software: SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Server and SAS Visual Analytics Duration: Five days Implement advanced methods for nominal variable selection and model assessment. Build advanced predictive models such as support vector machines and forests. Understand generalized profit matrices and assessment plots. Construct two-stage and component models. Use open source software in SAS Enterprise Miner. Prerequisites: It is recommended that students have completed the Applied Analytics Using SAS Enterprise Miner course, and have some experience with creating and managing SAS data sets, which can be learned in SAS Programming 1: Essentials. Students should also have some experience building statistical models using SAS/STAT software and have completed a statistics course that covers linear regression and logistic regression. Software: SAS Enterprise Miner Duration: Three days Applied Mixed Models Linear mixed models will be analyzed using the MIXED procedure. Special emphasis will be placed on hypotheses testing, repeated measures and missing data. There will be examples from the corporate world. Analyze data with random effects. Advanced Predictive Modeling Using SAS Enterprise Miner 13.1 This course covers advanced topics using SAS Enterprise Miner, including how to optimize the performance of predictive models beyond the basics. The course continues the development of predictive models that begins in the Applied Analytics Using SAS Enterprise Miner course (e.g., making use of the two-stage modeling node). In addition, some of the newest modeling nodes and latest variable selection methods are covered. Tips for working in an efficient way with SAS Enterprise Miner complete the course. Implement advanced methods for unsupervised dimension reduction. Implement advanced methods for supervised interval variable selection. Determine what is being tested for the fixed effects. Fit random coefficient models. Analyze repeated measures data. Perform residual and influence diagnostic analysis. Prerequisites: Students will be most successful with this material if they know how to create and manage SAS data sets; have experience performing analysis of variance using the GLM procedure of SAS/STAT software; have completed and mastered the Statistics 2: ANOVA and Regression course; or have completed a graduate-level course on general linear models. Software: SAS Enterprise Guide and SAS/STAT. Note that these are point-and-click interfaces to SAS; no programming required. Duration: Three days 4 Learn more at: sas.com/teach [email protected]

5 Partnering with SAS, we have provided our students an educational experience that allows them to successfully compete in a job market that covets students with advanced analytical skills. J. Michael Hardin Professor of Statistics University of Alabama our strategic partnership with SAS is the most important reason for the success of our data mining and analytics certificate program that has graduated more than 600 students in last 10 years. A combination of SAS and OSU certificate along with multiple SAS certifications make our candidates extremely attractive in the job marketplace. Dr. Goutam Chakraborty Professor (marketing) and Director of Graduate Certificate Program win Business Data Mining Oklahoma State University 5

6 Applied Analytics Using SAS Enterprise Miner This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association and sequence analyses) and predictive modeling (decision tree, regression and neural network models). Define a SAS Enterprise Miner project and explore data graphically. Modify data for better analysis results. Build and understand predictive models such as decision trees and regression models. Compare and explain complex models. Generate and use score code. Apply association and sequence discovery to transaction data. Prerequisites: Students should be acquainted with Microsoft Windows and Windows-based software. In addition, students should have at least an introductory-level familiarity with basic statistics and regression modeling. Previous SAS software experience is helpful but not required. Completion of the Data Manipulation and Analytics Using SAS Enterprise Guide course is also beneficial. Software: SAS Enterprise Miner Duration: Three days Data Manipulation and Analytics Using SAS Enterprise Guide This introduction to SAS Enterprise Guide is a hands-on workshop that shows students how to use the menu-driven tasks in SAS Enterprise Guide 6.1, the point-and-click interface to SAS. Students will learn how to perform common tasks such as: querying, reporting and analyzing data. Several statistical procedures will be used to analyze data and produce reports. SAS Enterprise Guide allows users to publish dynamic results in a Microsoft Windows client application. Demonstrations in the presentation will use research-type data and tasks in illustrating the functionality of SAS Enterprise Guide. Transform data. Explore data with tasks. Visualize data. Perform statistical analysis with tasks. Prerequisites: Students are most successful when they have completed one semester in statistics, including correlation, regression and distribution analysis. Software: SAS Enterprise Guide Duration: One day Introduction to SAS Visual Statistics This hands-on course provides an introduction to SAS Visual Statistics, a new web client that allows statisticians and data scientists to visually interact with and uncover insights from huge amounts of data. Students will perform powerful statistical modeling and machine-learning techniques via the easy-to-use, drag-and-drop visual interface. Interact with the environment via SAS Visual Analytics Hub and SAS Visual Statistics Hub. Explore data using SAS Visual Analytics Explorer. Create predictive models with SAS Visual Statistics. Create segmentation models with SAS Visual Statistics. Create decision trees with SAS Visual Statistics. Perform model comparisons with SAS Visual Statistics. Prerequisites: No SAS experience or programming experience is required, although students should have some computer experience. Specifically, students should be able to log on and log off a computer; use a keyboard or mouse; and use a web browser to access information. Software: SAS Visual Analytics, SAS Visual Statistics Duration: One day Navigate the SAS Enterprise Guide workspace. Build a typical workflow for data analysis. Work with data. Combine data with queries. 6 Learn more at: sas.com/teach [email protected]

7 SAS Visual Analytics for Professors This hands-on course examines the exploring and reporting capabilities of SAS Visual Analytics. The course provides a brief overview of the SAS Visual Analytics solution, then focuses on SAS Visual Analytics Explorer and SAS Visual Analytics Designer. SAS Visual Analytics Explorer is a web-based application that enables users to perform interactive data exploration. Its highly intuitive interface is backed by a high-performance, in-memory server that can execute analytics in real time across data in excess of a billion rows. Data can be explored via interactive visualizations such as charts, plots, histograms, tables and maps. Interact with the environment via the SAS Visual Analytics Hub. Explore data using the SAS Visual Analytics Explorer. Create reports with the SAS Visual Analytics Designer. View reports using the SAS Visual Analytics viewer and SAS Mobile BI. Prerequisites: No SAS experience or programming experience is required, although students should have some computer experience. Specifically, students should be able to log on and log off a computer; use a keyboard or mouse; and use a web browser to access information. Software: SAS Visual Analytics Duration: One day Text Analytics Using SAS Text Miner This course covers the functionality of SAS Text Miner, which is a separately licensed component available for SAS Enterprise Miner. In this course, you learn to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections, automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors. Process and prepare textual data for analysis. Convert unstructured character data into structured numeric data. Explore words and phrases in a document collection. Group documents using similarity measures. Find documents most closely associated with a word or phrase. Find words or phrases most closely associated with a document. Identify topics in a document collection. Classify documents based on derived or user-supplied topic definitions. Extract a subset of documents with term-based and stringbased query filters. Apply association discovery techniques to help understand the importance of noun phrases. Address problems from the areas of forensic linguistics, document categorization and information retrieval. Use textual data to improve predictive models. Prerequisites: Before attending this course, students should have experience using SAS Enterprise Miner to do pattern discovery and predictive modeling, or have completed the Applied Analytics Using SAS Enterprise Miner course. Software: SAS Text Analytics, SAS Text Miner Duration: Two days Using SAS to Clean Big Data In this workshop designed for SAS programmers, students learn techniques for finding errors in raw data or SAS data sets. These techniques involve using DATA step programming and other SAS procedures. Look for duplicates and n observations per subject. Check for values of character and variables. Check for missing values. Work with dates and multiple files. Perform double entry and verification. Use SQL for data cleansing. Correct errors, integrity constraints and audit trails. Prerequisites: Students should have knowledge and experience at the level of the SAS Programming 1: Essentials course. However, some of the programs discussed in the course use more advanced techniques such as FIRST and LAST, temporary variables and macro variables. Any SAS programming techniques that are beyond the SAS Programming 1: Essentials level are explained thoroughly in the course. Software: Base SAS or SAS Enterprise Guide Duration: Two days 7

8 About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. SAS Institute Inc. World Headquarters To contact your local SAS office, please visit: sas.com/offices SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright 2014, SAS Institute Inc. All rights reserved. S132149_1014