sas press

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.

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.

4月 252019
 

As a SAS programmer, you are asked to do many things with your data -- reading, writing, calculating, building interfaces, and occasionally sending data outside of SAS. One of the most popular outputs you may be tasked with creating is likely a Microsoft Excel workbook. Have you ever heard, “just send me the spreadsheet”?

For an internal project the task is easy, just open the SAS ODS EXCEL destination, run PROC PRINT, and close SAS ODS EXCEL and the workbook spreadsheet is ready. But if the workbook or the spreadsheet is to be delivered somewhere else you may need to spruce it up a bit. Of course, you can manually change virtually everything on the spreadsheet, but that takes lots of employee time. And if the spreadsheet is delivered on a periodic basis, you may not run it the same every time.

Saving you time and money

Suppose you run a PROC PRINT with a “BY” statement and produce a Microsoft Excel workbook with 100 pages. If each of those pages need to be printed and distributed to 100 clients by mail, do you want to be the person who changes each of those to print as a landscape printout? The SAS ODS Excel destination has over 125 options and sub-options that can perform various tasks while the workbook is being written. One such task sets the worksheet to print in “landscape” format.

As a programmer, I know that when I want to start a new project or learn new software, I look to two places in a book: the index and the table of contents. If I can think of a key word that might help me, I look to the index. But when searching general topics, I use the Table of Contents (TOC). It always frustrates me if the TOC is in alphabetical order, so I decided to write my TOC as groups of options and SAS commands that impacted similar parts or features of the Excel Workbook.

To see this in action, the bullet points I have listed below identify the major topic sections of the book. These are, in fact, chapter titles presented after the introduction:

    • ODS Tagset versus Destination
    • ODS Excel Destination Actions
    • Setting Excel Document Property Values
    • Options That Affect the Workbook
    • Arguments that Affect Output Features
    • Options That Affect Worksheet Features
    • Options That Affect Print Features
    • Column, Row, and Cell Features

Take a look inside

Allow me to describe each of these topics in a few words:

    ODS Tagset versus Destination
    Many people have used the SAS ODS Tagset EXCELXP and will find many parts of the SAS ODS EXCEL destination to be very similar in both syntax and function. A tagset is Proc template code that can be changed by the user, while a SAS ODS destination is a built-in feature of SAS, much like a PROC or FUNCTION that cannot be changed by the users. This section also describes the ID feature of ODS which allows you to write more than one EXCEL workbook at a time.

    ODS Excel Destination Actions
    This area describes ODS features that may not be exclusive to ODS EXCEL but are useful in finding and choosing SAS outputs to be processed.

    Setting Excel Document Property Values
    Here you are shown how to change the comments, keywords, author, title, and other parts of the Excel Property sheet.

    Options That Affect the Workbook
    This section of the book shows you how to name the workbook, create blank worksheets, create a table of contents or index of worksheets within the workbook, change worksheet tab colors, and other options.

    Arguments that Affect Output Features
    The output features described here include changing the output style (coloration of the worksheet sections), finding and using stylesheet anchors, building and using Cascading Style Sheets, changing the Dots Per Inch (DPI) of the output data and or graphs, and adding text to the worksheet.

    Options That Affect Worksheet Features
    SAS has many options that describe the output data. These include titles, footnotes, and byline text. Additionally, SAS can group output worksheets in many ways including by page, by proc, by table, by by-group, or even no separation at all. Data can also be set to “FITTOPAGE” or the height or width can be selected along with adding sheet names or labels to the EXCEL output worksheets.

    Options That Affect Print Features
    Excel has many print features like printing in “black and white” only, centering horizontally or vertically, landscape or portrait, draft quality or standard, selecting the print order of the data, selecting the area to print, EXCEL headers and EXCEL footnotes, and others. All of which SAS can adjust as the workbook is being written.

    Column, Row, and Cell Features
    Finally, SAS can adjust column and row features like adding filters, changing the widths and heights or rows and columns, hiding rows or columns, inserting formulas, and even placing the data somewhere other than row one column one.

Ready to see the full Table of Contents? Click here.

Ready, set, go

My book Exchanging Data From SAS® to Excel: The ODS Excel Destination expands upon the SAS documentation by giving full descriptions and examples including SAS code and EXCEL output for nearly every option and sub-option of the SAS ODS EXCEL software. In addition to this blog, check out a free chapter of my book to get started making your worksheets beautifully formatted. Get ready to follow the money and make your reports come out perfect for publication in no time!

Making the most of the ODS Excel destination was published on SAS Users.

4月 162019
 

This blog post is based on the Code Snippets tutorial video in the free SAS® Viya® Enablement course from SAS Education. Keep reading to learn more about code snippets or check out the video to follow along with the tutorial in real-time.

Has there ever been a block of code that you use so infrequently that you always seem to forget the options that you need? Conversely, has there ever been a block of code that you use so frequently that you grow tired of typing it all the time? Code snippets can greatly assist with both of these scenarios. In this blog post, we discuss using pre-installed code snippets and creating new code snippets within SAS Viya.

Pre-installed code snippets

Figure 1: Pre-installed Snippets

SAS Viya comes with several code snippets pre-installed, including snippets to connect to CAS. To access these snippets, expand the Snippets area on the left navigation panel of SAS Studio as shown in Figure 1. You can see that the snippets are divided into categories, making it easier to find them.

If you double-click a pre-installed code snippet, or if you click and drag the snippet into the code editor panel, then the snippet will appear in the panel.

Snippets can range from very simple to very complex. Some contain comments. Some contain macro variables. Some might be only a couple of lines of code. That is the advantage of snippets. They can be anything that you want them to be.

 

 

Create new snippets

Now, let’s create a snippet of our own. Figure 2 shows an example of code that calls PROC CARDINALITY. This code is complete and fully executable. When you have the code the way that you want in your code window, click on the shortcut button for Add to My Snippets above the code. The button is outlined in a box in Figure 2.

Figure 2: Add to My Snippets Button

A window will appear that asks you to name the snippet. Naming the snippet then saves it into the My Snippets area in the left navigation panel for future use.

Remember that snippets are extremely flexible. The code that you save does not have to be fully executable. Instead of supplying the data source in your code, you may instead include notes or comments about what needs to be added, which makes the code more general, but it is still a very useful snippet.

To use one of your saved snippets, simply navigate to the My Snippets area, then double-click on your snippet or drag it into the code window.

Want to learn more about SAS Viya? Download the free e-book Exploring SAS® Viya®: Programming and Data Management. The content in this e-book is based on SAS® Viya® Enablement," a free course available from SAS Education.

Using code snippets in SAS® Viya® was published on SAS Users.