sas books

9月 082019
 


Today, September 8th, is International Literacy Day! A day celebrated by UNESCO since 1967 to emphasize the importance of literacy around the world. Here at SAS, we have decided to highlight data literacy, a critical part of our evolving knowledge as data and analytics continue to dominate the way we do business.

SAS Press author Susan Slaughter defines data literacy as, “understanding that data are not dry, dusty, abstract squiggles on a computer screen, but represent living things: people, plants, animals. You know you are fluent in a foreign language when you are comfortable speaking it and can communicate what you want to say. The same is true for data literacy; it is about reaching a level of comfort, about being able to communicate what is important to you, and about seeing the meaning behind the data.

Everyone knows that technology is becoming more and more a part of everyday life. Without data literacy, people become passive recipients; with data literacy, you can actively engage with technology. SAS calls it ‘the power to know’ and that's an accurate description.”

SAS Press has been helping users be more fluent in data literacy for almost 30 years! The Little SAS Book is about to publish its sixth edition and has been helping programmers learn SAS and analyze their data since 1995.

Free SAS Press e-books

To celebrate national literacy day and do our part in sharing about data literacy, SAS Press would like to share with you our free e-books on a range of topics related to data analytics. These books focus on topics such as text analytics, data management, AI, and Machine Learning.

Moving to the cloud?

Looking for information on SAS Viya? Download our two new free e-books on Exploring SAS Viya. Both books cover the features and capabilities of SAS Viya. SAS Viya extends the SAS platform to enable everyone – data scientists, business analysts, developers, and executives alike – to collaborate and realize innovative results faster.

Here is a list of our free e-books on SAS Viya:

Exploring SAS ®Viya®: Programming and Data Management
This first book in the series covers how to access data files, libraries, and existing code in SAS® Studio. You also will learn about new procedures in SAS Viya, how to write new code, and how to use some of the pre-installed tasks that come with SAS® Visual Data Mining and Machine Learning.

Exploring SAS® Viya®: Visual Analytics, Statistics, and Investigations
Data visualization enables decision-makers to see analytics presented visually so that they can grasp difficult concepts or identify new patterns. This book includes four visualization solutions powered by SAS Viya: SAS Visual Analytics, SAS Visual Statistics, SAS Visual Text Analytics, and SAS Visual Investigator.

Interested in learning more?

As becoming more data literate becomes increasingly more important in our daily lives, knowing where to get new information and tools to learn becomes critical to innovation and change. To stay up-to-date on new SAS Press books and our new free e-books releases, subscribe to our monthly newsletter.

Celebrating #InternationalLiteracyDay with Free SAS E-books! was published on SAS Users.

9月 072019
 

By 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value. – Gartner

What is data literacy?

Data literacy is the ability to read, work with, analyze, and argue with data. – Wikipedia

Data literacy is the ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word. – WhatIs.com

Why is it important?

As data and analytics become core to the enterprise, and data becomes an organizational asset, employees must have at least a basic ability to communicate and understand conversations about data. Just as it is a given that employees are now competent in word processing and spreadsheets, the ability to “speak data” will become an integral aspect of most day-to-day jobs.

Gone will be the days when data scientists, analysts, and statisticians are the only ones “speaking data.” Valerie Logan, Senior Director Analyst, Gartner, says workforce data literacy must treat information as a second language. Just as we expect all employees today to have a basic level of computer literacy, use email, and understand spreadsheets, employees will also need to be able to understand and speak basic data.

Chris Hemedinger, author of SAS for Dummies, touched on this in his blog a skeptics guide to statistics in the media. He is old enough to remember when USA Today began publication in the early 1980s. He remembers scanning each edition for the USA Today Snapshots, a mini infographic feature that presented some statistics in a fun and interesting way. “Back then, I felt that these stats made me a little bit smarter for the day. I had no reason to question the numbers I saw, nor did I have the tools, skill, or data access to check their work.”

Chris warns that as more and more “news articles and editorial pieces often use simplified statistics to convey a message or support an argument,” we will need to learn that “statistics in the media should not be accepted at face value.” Learning to analyze and understand data and statistics will become increasingly more vital for future generations.

Best-selling SAS Press author, Ron Cody, cautions that with the augmented technology that allows non-programmers to be able to run complex programs to search databases, summarize data, and conduct statistical tests, it is vital that everyone has a basic understanding of the data and analytics behind the results. “With advances in artificial intelligence, we may be able to tell the computer our problem and let it solve it and tell us the answer.” With technology advancing so quickly with AI, we will all need to understand the data and avoid including bias into our models. Misunderstood data can negatively influence AI algorithms or interpretation of models.

The future

Tom Fisher, Senior Vice President of Business Development at SAS explains, “the convergence of model management with data management represents one of the most exciting business opportunities of the future. The merging and blending of these two disciplines should enable the elimination of bias that may occur in the collection and aggregation of data.” Initiatives such as MIT’s Data Nutrition Project address the missing step in the model development pipeline, “assessing data sets based on standard quality measures that are both qualitative and quantitative.” As Fisher concludes, “these kinds of approaches are designed to allow consumers of data, as input to models, to have a more complete understanding of the data that’s being ingested. At the end of the day, the goal of these integrated disciplines is to provide greater accuracy and comfort with the result sets that are being delivered by data scientists and data engineers.”

As the Gartner report quoted earlier notes, as organizations become more data-driven, poor data literacy will become an inhibitor to growth. But not everyone wants to be a statistician or data scientist. This is where the analogy to computer literacy parts ways. We don’t all have to have a statistics degree – AI can help. SAS is developing solutions where AI is augmented into its most sophisticated and powerful solutions to give everyone data literacy. For example, SAS® Model Manager looks at the data and the problem to suggest models. It can then choose the best model based on the user’s criteria, test the model, and score. Technology to report and explain the results, and even answer questions is under development – all in natural language! A virtual personal assistant who can “speak data” and translate.

While data literacy will become increasingly important, so too will tools to help moderate and translate the data that will continue to drive our enterprises and our lives.

Resources:
Become more data literate with our library of Getting Started with SAS, Statistics, Machine Learning, and Data Management books. Visit SAS Books.

Explore SAS Analytics Industry Solutions at sas.com/industry.

Why we need to learn how to "speak data" in a data-driven future was published on SAS Users.

7月 032019
 

One of my favorite parts of summer is a relaxing weekend by the pool. Summer is the time I get to finally catch up on my reading list, which has been building over the year. So, if expanding your knowledge is a goal of yours this summer, SAS Press has a shelf full of new titles for you to explore. To help navigate your selection we asked some of our authors what SAS books were on their reading lists for this summer?

Teresa Jade


Teresa Jade, co-author of SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models, has already started The DS2 Procedure: SAS Programming Methods at Work by Peter Eberhardt. Teresa reports that the book “is a concise, well-written book with good examples. If you know a little bit about the SAS DATA step, then you can leverage what you know to more quickly get up to speed with DS2 and understand the differences and benefits.”
 
 
 

Derek Morgan

Derek Morgan, author of The Essential Guide to SAS® Dates and Times, Second Edition, tells us his go-to books this summer are Art Carpenter’s Complete Guide to the SAS® REPORT Procedure and Kirk Lafler's PROC SQL: Beyond the Basics Using SAS®, Third Edition. He also notes that he “learned how to use hash objects from Don Henderson’s Data Management Solutions Using SAS® Hash Table Operations: A Business Intelligence Case Study.”
 

Chris Holland

Chris Holland co-author of Implementing CDISC Using SAS®: An End-to-End Guide, Revised Second Edition, recommends Richard Zink’s JMP and SAS book, Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP® and SAS®, which describes how to improve efficiency while reducing costs in trials with centralized monitoring techniques.
 
 
 
 
 

And our recommendations this summer?

Download our two new free e-books which illustrate the features and capabilities of SAS® Viya®, and SAS® Visual Analytics on SAS® Viya®.

Want to be notified when new books become available? Sign up to receive information about new books delivered right to your inbox.

Summer reading – Book recommendations from SAS Press authors was published on SAS Users.

6月 182019
 

What is Item Response Theory?

Item Response Theory (IRT) is a way to analyze responses to tests or questionnaires with the goal of improving measurement accuracy and reliability.

A common application is in testing a student’s ability or knowledge. Today, all major psychological and educational tests are built using IRT. The methodology can significantly improve measurement accuracy and reliability while providing potential significant reductions in assessment time and effort, especially via computerized adaptive testing. For example, the SAT and GRE both use Item Response Theory for their tests. IRT takes into account the number of questions answered correctly and the difficulty of the question.

In recent years, IRT models have also become increasingly popular in health behavior, quality of life, and clinical research. There are many different models for IRT. Three of the most popular are:

The Rasch model

Two-parameter model

Graded Response model

Early IRT models (such as the Rasch model and two-parameter model) concentrate mainly on dichotomous responses. These models were later extended to incorporate other formats, such as ordinal responses, rating scales, partial credit scoring, and multiple category scoring.

Item Response Theory Models Using SAS

Ron Cody and Jeffrey K. Smith’s book, Test Scoring and Analysis Using SAS, uses SAS PROC IRT to show how to develop your own multiple-choice tests, score students, produce student rosters (in print form or Excel), and explore item response theory (IRT).

Aimed at non-statisticians working in education or training, the book describes item analysis and test reliability in easy-to-understand terms and teaches SAS programming to score tests, perform item analysis, and estimate reliability.

For those with a more statistical background, Bayesian Analysis of Item Response Theory Models Using SAS describes how to estimate and check IRT models using the SAS MCMC procedure. Written especially for psychometricians, scale developers, and practitioners, numerous programs are provided and annotated so that you can easily modify them for your applications.

Assessment has played, and continues to play, an integral part in our work and educational settings. IRT models continue to be increasingly popular in many other fields, such as medical research, health sciences, quality-of-life research, and even marketing research. With the use of IRT models, you can not only improve scoring accuracy but also economize test administration by adaptively using only the discriminative items.

Interested in learning more? Check out our chapter previews available for free. Want to learn more about SAS Press? Explore our online bookstore and subscribe to our newsletter to get all the latest discounts, news, and more.

Further resources

SAS Blogs:
New at SAS: Psychometric Testing by Charu Shankar
SAS author’s tip: Bayesian analysis of item response theory models

SAS Communities:
SAS Communities: Custom Task Tuesday: SAS Global Forum/PROC IRT Edition!

SAS Global Forum Paper:
Item Response Theory: What It Is and How You Can Use the IRTProcedure to Apply It by Xinming An and Yiu-Fai Yung

SAS Documentation:
The IRT Procedure
SAS/STAT 14.1 User Guide: The IRT Procedure
SAS/STAT 14.2 User Guide: Help Center

Understanding Item Response Theory with SAS was published on SAS Users.

6月 062019
 

Want to learn SAS programming but worried about taking the plunge? Over at SAS Press, we are excited about an upcoming publication that introduces newbies to SAS in a peer-review instruction format we have found popular for the classroom. Professors Jim Blum and Jonathan Duggins have written Fundamentals of Programming in SAS using a spiral curriculum that builds upon topics introduced earlier and at its core, uses large-scale projects presented as case studies. To get ready for release, we interviewed our new authors on how their title will enhance our SAS Books collection and which of the existing SAS titles has had an impact on their lives!

What does your book bring to the SAS collection? Why is it needed?

Blum & Duggins: The book is probably unique in the sense that it is designed to serve as a classroom textbook, though it can also be used as a self-study guide. That also points to why we feel it is needed; there is no book designed for what we (and others) do in the classroom. As SAS programming is a broad topic, the goal of this text is to give a complete introduction of effective programming in Base SAS – covering topics such as working with data in various forms and data manipulation, creating a variety of tabular and visual summaries of data, and data validation and good programming practices.

The book pursues these learning objectives using large-scale projects presented as case studies. The intent of coupling the case-study approach with the introductory programming topics is to create a path for a SAS programming neophyte to evolve into an adept programmer by showing them how programmers use SAS, in practice, in a variety of contexts. The reader will gain the ability to author original code, debug pre-existing code, and evaluate the relative efficiencies of various approaches to the same problem using methods and examples supported by pedagogical theory. This makes the text an excellent companion to any SAS programming course.

What is your intended audience for the book?

Blum & Duggins: This text is intended for use in both undergraduate and graduate courses, without the need for previous exposure to SAS. However, we expect the book to be useful for anyone with an aptitude for programming and a desire to work with data, as a self-study guide to work through on their own. This includes individuals looking to learn SAS from scratch or experienced SAS programmers looking to brush up on some of the fundamental concepts. Very minimal statistical knowledge, such as elementary summary statistics (e.g. means and medians), would be needed. Additional knowledge (e.g. hypothesis testing or confidence intervals) could be beneficial but is not expected.

What SAS book(s) changed your life? How? And why?

Blum: I don’t know if this qualifies, but the SAS Programming I and SAS Programming II course notes fit this best. With those, and the courses, I actually became a SAS programmer instead of someone who just dabbled (and dabbled ineffectively). From there, many doors were opened for me professionally and, more importantly, I was able to start passing that knowledge along to students and open some doors for them. That experience also served as the basis for building future knowledge and passing it along, as well.

Duggins: I think the two SAS books that most changed my outlook on programming (which I guess has become most of my life, for better or worse) would either be The Essential PROC SQL Handbook for SAS Users by Katherine Prairie or Jane Eslinger's The SAS Programmer's PROC REPORT Handbook, because I read them at different times in my SAS programming career. Katherine's SQL book changed my outlook on programming because, until then, I had never done enough SQL to consistently consider it as a viable alternative to the DATA step. I had taken a course that taught a fair amount of SQL, but since I had much more experience with the DATA step and since that is what was emphasized in my doctoral program, I didn't use SQL all that often. However, after working through her book, I definitely added SQL to my programming arsenal. I think learning it, and then having to constantly evaluate whether the DATA step or SQL was better suited to my task, made me a better all-around programmer.

As for Jane's book - I read it much later after having used some PROC REPORT during my time as a biostatistician, but I really wasn't aware of how much could be done with it. I've also had the good fortune to meet Jane, and I think her personality comes through clearly - which makes that book even more enjoyable now than it was during my first read!

Read more

We at SAS Press are really excited to add this new release to our collection and will continue releasing teasers until its publication. For almost 30 years SAS Press has published books by SAS users for SAS users. Here is a free excerpt located on our Duggins' author page to whet your appetite. (Please know that this excerpt is an unedited draft and not the final content). Look out for news on this new publication, you will not want to miss it!

Want to find out more about SAS Press? For more about our books, subscribe to our newsletter. You’ll get all the latest news and exclusive newsletter discounts. Also, check out all our new SAS books at our online bookstore.

Interview with new SAS Press authors: Jim Blum and Jonathan Duggins was published on SAS Users.

5月 232019
 

Often, the SAS 9.4 administration environment architecture can seem confusing to new administrators. You may be faced with questions like: What is a tier? Why are there so many servers? What is the difference between distributed and non-distributed installations?

Understanding SAS 9.4 architecture is key to tackling the tasks and responsibilities that come with SAS administration and will help you know where to look to make changes or troubleshoot problems. One of the ways I have come to think about SAS 9.4 architecture is to think of it like building a house.

So, what is the first thing you need to build a house? Besides money and a Home Depot rewards credit card, land is the first thing you need to put the house on. For SAS administration the land is your infrastructure and hardware, and the house you want to build on that land is your SAS software. You, the admin, are the architect. Sometimes building a house can be simple, so only one architect is needed. Other times, for more complex buildings, an entire team of architects is needed to keep things running smoothly.

Once the architect decides how the house should look and function, and the plans are signed off, the foundation is laid. In our analogy, this foundation is the SAS metadata server – the rest of the installation sits on top of it.

Next come the walls and ceilings for either a single-story ranch house (a distributed SAS environment) or a multi-story house (a non-distributed SAS environment). Once the walls are painted, the plumbing installed, and the carpets laid, you have a house made up of different rooms. Each room has a task: a kitchen to make food, a child’s bedroom to sleep in, and a living room to relax and be with family. Each floor and each room serve the same purpose as a SAS server – each server is dedicated to a specific task and has a specific purpose.
Finally, all of the items in each room, such as the bed, toys, and kitchen utensils can be equated to a data source: like a SAS data set, data pulled in from Hadoop or an Excel spreadsheet. Knowing what is in each room helps you find objects by knowing where they should belong.

Once you move into a house, though, the work doesn’t stop there, and the same is true for a SAS installation. Just like the upkeep on a house (painting the exterior, fixing appliances when they break, etc.), SAS administration requires maintenance to keep everything running smoothly.

How this relates to SAS

To pull this analogy back to SAS, let us start with the different install flavors (single house versus townhouse, single story versus multiple stories). SAS can be installed either as a SAS Foundation install or as a metadata-managed install. A SAS Foundation install is the most basic (think Base SAS). A metadata-managed install is the SAS 9 Intelligence Platform, with many more features and functionality than Base SAS. With SAS Foundation, your users work on their personal machines or use Remote Desktop or Citrix. A SAS Foundation install does not involve a centrally metadata managed system, however in a metadata managed install, your users work on the dedicated SAS server. These two different SAS deployments can be installed on physical or virtual machines, and all SAS solution administration is based off of SAS 9.4 platform administration.

We hope you find this overview of SAS platform administration helpful. For more information check out this list of links to additional admin resources from my new book, SAS® Administration from the Ground Up: Running the SAS®9 Platform in a Metadata Server Environment.

SAS 9.4 architecture – building an installation from the ground up was published on SAS Users.

5月 172019
 

As a publishing house inside of SAS, we often hear: “Does anyone want to read books anymore?” Especially technical programmers who are “too busy” to read. About a quarter of American adults (24%) say they haven’t read a book in whole or in part in the past year, whether in print, electronic or audio form. In addition, leisure reading is at an all-time low in the US. However, we know that as literacy expansion throughout the world has grown, it has also helped reduce inequalities across and within countries. Over the years many articles have been published about how books will soon become endangered species, but can we let that happen when we know the important role books play in education?

At SAS, curiosity and life-long learning are part of our culture. All employees are encouraged to grow their skill set and never stop learning! While different people do have different preferred learning styles, statistics show that reading is critical to the development of life-long learners, something we agree with at SAS Press:

  • In a study completed at Yale University, Researchers studied 3,635 people older than 50 and found that those who read books for 30 minutes daily lived an average of 23 months longer than nonreaders or magazine readers. The study stated that the practice of reading books creates a cognitive engagement that improves a host of different things including vocabulary, cognitive skills, and concentration. Reading can also affect empathy, social perception, and emotional intelligence, which all help people stay on the planet longer.
  • Vocabulary is notoriously resistant to aging, and having a vast one, according to researchers from Spain’s University of Santiago de Compostela, can significantly delay the manifestation of mental decline. When a research team at the university analyzed vocabulary test scores of more than 300 volunteers ages 50 and older, they found that participants with the lowest scores were between three and four times more at risk of cognitive decay than participants with the highest scores.
  • One international study of long-term economic trends among nations found that, along with math and science, “reading performance is strongly and significantly related to economic growth.”

Putting life-long learning into practice

Knowing the importance that reading plays, not only in adult life-long learning with books, SAS has been working hard to improve reading proficiency in young learners — which often ties directly to the number of books in the home, the number of times parents read to young learners, and the amount adults around them read themselves.

High-quality Pre-K lays the foundation for third-grade reading proficiency which is critical to future success in a knowledge-driven economy. — Dr. Jim Goodnight

With all the research pointing to why reading is so important to improving your vocabulary and mental fortitude, it seems only telling that learning SAS through our example-driven, in-depth books would prove natural.

So to celebrate #endangeredspecies day and help save what some call an “endangered species,” let’s think about:

  • What SAS books have you promised yourself you would read this year?
  • What SAS books will you read to continue your journey as a life-long learner?
  • What book do you think will get you to the next level of your SAS journey?

Let us know in the comments, what SAS book improved your love of SAS and took you on a life-long learner journey?

For almost thirty years SAS Press has published books by SAS users for SAS users. Want to find out more about SAS Press? For more about our books and some more of our SAS Press fun, subscribe to our newsletter. You’ll get all the latest news and exclusive newsletter discounts. Also, check out all our new SAS books at our online bookstore.

Other Resources:
About SAS: Education Outreach
About SAS: Reading Proficiency
Poor reading skills stymie children and the N.C. economy by Dr. Jim Goodnight

Do books count as endangered species? was published on SAS Users.

5月 142019
 

Interested in making business decisions with big data analytics? Our Wiley SAS Business Series book Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value by Bart Baesens, Wouter Verbeke, and Cristian Danilo Bravo Roman has just the information you need to learn how to use SAS to make data and analytics decision-making a part of your core business model!

This book combines the authorial team’s worldwide consulting experience and high-quality research to open up a road map to handling data, optimizing data analytics for specific companies, and continuously evaluating and improving the entire process.

In the following excerpt from their book, the authors describe a value-centric strategy for using analytics to heighten the accuracy of your enterprise decisions:

“'Data is the new oil' is a popular quote pinpointing the increasing value of data and — to our liking — accurately characterizes data as raw material. Data are to be seen as an input or basic resource needing further processing before actually being of use.”

Analytics process model

In our book, we introduce the analytics process model that describes the iterative chain of processing steps involved in turning data into information or decisions, which is quite similar actually to an oil refinery process. Note the subtle but significant difference between the words data and information in the sentence above. Whereas data fundamentally can be defined to be a sequence of zeroes and ones, information essentially is the same but implies in addition a certain utility or value to the end user or recipient.

So, whether data are information depends on whether the data have utility to the recipient. Typically, for raw data to be information, the data first need to be processed, aggregated, summarized, and compared. In summary, data typically need to be analyzed, and insight, understanding, or knowledge should be added for data to become useful.

Applying basic operations on a dataset may already provide useful insight and support the end user or recipient in decision making. These basic operations mainly involve selection and aggregation. Both selection and aggregation may be performed in many ways, leading to a plentitude of indicators or statistics that can be distilled from raw data. Providing insight by customized reporting is exactly what the field of business intelligence (BI) is about.

Business intelligence is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to — and analysis of — information to improve and optimize decisions and performance.

This model defines the subsequent steps in the development, implementation, and operation of analytics within an organization.

    Step 1
    As a first step, a thorough definition of the business problem to be addressed is needed. The objective of applying analytics needs to be unambiguously defined. Some examples are: customer segmentation of a mortgage portfolio, retention modeling for a postpaid Telco subscription, or fraud detection for credit cards. Defining the perimeter of the analytical modeling exercise requires a close collaboration between the data scientists and business experts. Both parties need to agree on a set of key concepts; these may include how we define a customer, transaction, churn, or fraud. Whereas this may seem self-evident, it appears to be a crucial success factor to make sure a common understanding of the goal and some key concepts is agreed on by all involved stakeholders.

    Step 2
    Next, all source data that could be of potential interest need to be identified. The golden rule here is: the more data, the better! The analytical model itself will later decide which data are relevant and which are not for the task at hand. All data will then be gathered and consolidated in a staging area which could be, for example, a data warehouse, data mart, or even a simple spreadsheet file. Some basic exploratory data analysis can then be considered using, for instance, OLAP facilities for multidimensional analysis (e.g., roll-up, drill down, slicing and dicing).

    Step 3
    After we move to the analytics step, an analytical model will be estimated on the preprocessed and transformed data. Depending on the business objective and the exact task at hand, a particular analytical technique will be selected and implemented by the data scientist.

    Step 4
    Finally, once the results are obtained, they will be interpreted and evaluated by the business experts. Results may be clusters, rules, patterns, or relations, among others, all of which will be called analytical models resulting from applying analytics. Trivial patterns (e.g., an association rule is found stating that spaghetti and spaghetti sauce are often purchased together) that may be detected by the analytical model is interesting as they help to validate the model. But of course, the key issue is to find the unknown yet interesting and actionable patterns (sometimes also referred to as knowledge diamonds) that can provide new insights into your data that can then be translated into new profit opportunities!

    Step 5
    Once the analytical model has been appropriately validated and approved, it can be put into production as an analytics application (e.g., decision support system, scoring engine). Important considerations here are how to represent the model output in a user-friendly way, how to integrate it with other applications (e.g., marketing campaign management tools, risk engines), and how to make sure the analytical model can be appropriately monitored and back-tested on an ongoing basis.

Book giveaway!

If you are as excited about business analytics as we are and want a copy of Bart Baesens’ book Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value, enter to win a free copy in our book giveaway today! The first 5 commenters to correctly answer the question below get a free copy of Baesens book! Winners will be contacted via email.

Here's the question:
What Free SAS Press e-book did Bart Baesens write the foreword too?

We look forward to your answers!

Further resources

Want to prove your business analytics skills to the world? Check out our Statistical Business Analyst Using SAS 9 certification guide by Joni Shreve and Donna Dea Holland! This certification is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis.

For more information about the certification and certification prep guide, watch this video from co-author Joni Shreve on their SAS Certification Prep Guide: Statistical Business Analysis Using SAS 9.

Big data in business analytics: Talking about the analytics process model was published on SAS Users.

5月 142019
 

Interested in making business decisions with big data analytics? Our Wiley SAS Business Series book Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value by Bart Baesens, Wouter Verbeke, and Cristian Danilo Bravo Roman has just the information you need to learn how to use SAS to make data and analytics decision-making a part of your core business model!

This book combines the authorial team’s worldwide consulting experience and high-quality research to open up a road map to handling data, optimizing data analytics for specific companies, and continuously evaluating and improving the entire process.

In the following excerpt from their book, the authors describe a value-centric strategy for using analytics to heighten the accuracy of your enterprise decisions:

“'Data is the new oil' is a popular quote pinpointing the increasing value of data and — to our liking — accurately characterizes data as raw material. Data are to be seen as an input or basic resource needing further processing before actually being of use.”

Analytics process model

In our book, we introduce the analytics process model that describes the iterative chain of processing steps involved in turning data into information or decisions, which is quite similar actually to an oil refinery process. Note the subtle but significant difference between the words data and information in the sentence above. Whereas data fundamentally can be defined to be a sequence of zeroes and ones, information essentially is the same but implies in addition a certain utility or value to the end user or recipient.

So, whether data are information depends on whether the data have utility to the recipient. Typically, for raw data to be information, the data first need to be processed, aggregated, summarized, and compared. In summary, data typically need to be analyzed, and insight, understanding, or knowledge should be added for data to become useful.

Applying basic operations on a dataset may already provide useful insight and support the end user or recipient in decision making. These basic operations mainly involve selection and aggregation. Both selection and aggregation may be performed in many ways, leading to a plentitude of indicators or statistics that can be distilled from raw data. Providing insight by customized reporting is exactly what the field of business intelligence (BI) is about.

Business intelligence is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to — and analysis of — information to improve and optimize decisions and performance.

This model defines the subsequent steps in the development, implementation, and operation of analytics within an organization.

    Step 1
    As a first step, a thorough definition of the business problem to be addressed is needed. The objective of applying analytics needs to be unambiguously defined. Some examples are: customer segmentation of a mortgage portfolio, retention modeling for a postpaid Telco subscription, or fraud detection for credit cards. Defining the perimeter of the analytical modeling exercise requires a close collaboration between the data scientists and business experts. Both parties need to agree on a set of key concepts; these may include how we define a customer, transaction, churn, or fraud. Whereas this may seem self-evident, it appears to be a crucial success factor to make sure a common understanding of the goal and some key concepts is agreed on by all involved stakeholders.

    Step 2
    Next, all source data that could be of potential interest need to be identified. The golden rule here is: the more data, the better! The analytical model itself will later decide which data are relevant and which are not for the task at hand. All data will then be gathered and consolidated in a staging area which could be, for example, a data warehouse, data mart, or even a simple spreadsheet file. Some basic exploratory data analysis can then be considered using, for instance, OLAP facilities for multidimensional analysis (e.g., roll-up, drill down, slicing and dicing).

    Step 3
    After we move to the analytics step, an analytical model will be estimated on the preprocessed and transformed data. Depending on the business objective and the exact task at hand, a particular analytical technique will be selected and implemented by the data scientist.

    Step 4
    Finally, once the results are obtained, they will be interpreted and evaluated by the business experts. Results may be clusters, rules, patterns, or relations, among others, all of which will be called analytical models resulting from applying analytics. Trivial patterns (e.g., an association rule is found stating that spaghetti and spaghetti sauce are often purchased together) that may be detected by the analytical model is interesting as they help to validate the model. But of course, the key issue is to find the unknown yet interesting and actionable patterns (sometimes also referred to as knowledge diamonds) that can provide new insights into your data that can then be translated into new profit opportunities!

    Step 5
    Once the analytical model has been appropriately validated and approved, it can be put into production as an analytics application (e.g., decision support system, scoring engine). Important considerations here are how to represent the model output in a user-friendly way, how to integrate it with other applications (e.g., marketing campaign management tools, risk engines), and how to make sure the analytical model can be appropriately monitored and back-tested on an ongoing basis.

Book giveaway!

If you are as excited about business analytics as we are and want a copy of Bart Baesens’ book Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value, enter to win a free copy in our book giveaway today! The first 5 commenters to correctly answer the question below get a free copy of Baesens book! Winners will be contacted via email.

Here's the question:
What Free SAS Press e-book did Bart Baesens write the foreword too?

We look forward to your answers!

Further resources

Want to prove your business analytics skills to the world? Check out our Statistical Business Analyst Using SAS 9 certification guide by Joni Shreve and Donna Dea Holland! This certification is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis.

For more information about the certification and certification prep guide, watch this video from co-author Joni Shreve on their SAS Certification Prep Guide: Statistical Business Analysis Using SAS 9.

Big data in business analytics: Talking about the analytics process model was published on SAS Users.

5月 102019
 

May 12th is #NationalLimerickDay! If you saw our Valentine’s Day poem, you know we at SAS Press love creating poems and fun rhymes, so check out our limericks below!

So, what’s a limerick?

National Limerick Day is observed each year on May 12th and honors the birthday of the famed English artist, illustrator, author and poet Edward Lear (May 12, 1812 – Jan. 29, 1888). Lear’s poetry is most famous for its nonsense or absurdity, and mostly consists of prose and limericks.

His book, “Book of Nonsense,” published in 1846 popularized the limerick poem.

A limerick poem has five lines and is often very short, humorous, and full of nonsense. To create a limerick the first two lines must rhyme with the fifth line, and the third and fourth lines rhyme together. The limerick’s rhythm is officially described as anapestic meter.

To celebrate, we want to ask all lovers of SAS books to enjoy the limericks written by us and to see if you can create your own! Can you top our limericks on our love for SAS Books? Check out our handy how-to limerick links below.

Our limericks

There once was a software named SAS
helping tons of analysts complete tasks.
a Text Analytics book to extract meaning as data flies by
and a Portfolio and Investment Analysis book so you’ll never go awry.
You know our SAS books are first-class!

We enjoyed meeting our awesome users at SAS Global Forum
who enjoy our books with true decorum.
a SAS Administration book on building from the ground up
and a new book about PROC SQL you need to pick-up.
Checkout our SAS books today, you’ll adore ‘em!

For more about SAS Books and some more of our SAS Press fun, subscribe to our newsletter. You’ll get all the latest news and exclusive newsletter discounts. Also check out all our new SAS books at our online bookstore.

Resources:
Wiki-How: How to Write A Limerick
Limerick Generator: Create a Limerick in Seconds

Happy National Limerick Day from SAS Press! was published on SAS Users.