analytics

10月 152020
 

ModelOps is NOT a one-size-fits-all approach. It's important to identify the appropriate level of sophistication based on both the organization’s current readiness and its current and future business needs. My previous articles on Medium talked about why organizations should choose ModelOps, and how to implement ModelOps using a holistic approach [...]

Developing ModelOps sophistication: Choosing the right level was published on SAS Voices by Reece Clifford

10月 152020
 

Being overwhelmed by the volume of news isn’t a new phenomenon. But today, our sense of being overwhelmed has increased and triggered feelings of fear, frustration and anxiety, given the ongoing developments and research tied to COVID-19. How do we sift through the volume of information facing us and truly understand whether the news we consume is factual or based on [...]

Sanitize before you share: How SAS is using data to make sense of COVID-19 was published on SAS Voices by Lee Ellen Harmer

9月 222020
 

Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya?

SAS has been helping programmers and coders build complex machine learning models and solve complex business problems over many years.

Building on the vision and commitment to democratize analytics, SAS Viya offers multiple ways to support non-programmers and empowers people with no programming skills to get up and running quickly and build machine learning models. I touched on some of the ways this can be done via SAS Visual Analytics in my previous post on analytics for everyone with SAS Viya. In addition, SAS Viya also supports more advanced pipeline-based visual modeling via SAS Visual Data Mining and Machine Learning. The combination of these different tools within SAS Viya supporting a low-code/no-code approach to modeling makes SAS Viya an incredibly flexible and powerful analytics platform that can help drive analytics usage and adoption throughout an organization.

As analytics and machine learning become more pervasive, an analytics platform that supports a low-code/no-code approach can get more people involved, drive ongoing innovations, and ultimately accelerate digital transformation throughout an organization.

Speed

I have met my fair share of coding ninjas who blew me away with their ability to build models using keyboards with lightning speed. But when it comes to being able to quickly get an idea into a model and generate all the assessment statistics and charts, there is nothing quite like a visual approach to building machine learning models.

In SAS Viya, you can build a decision tree model literally just by dragging and dropping the relevant variables onto the canvas as shown in the animated screen flow below.

Building a machine learning model via drag and drop

In this case, we were able to quickly build a decision tree model that predicts child mortality rates around the world. Not only do we get the decision tree in all its graphics glory (on the left-hand side of the image), we also get the overall model fit measure (Average Standard Error in this case), a variable importance chart, as well as a lift chart all without having to enter a single line of code in under 5 seconds!

You also get a bunch of detailed statistical outputs, including a detailed node statistics table without having to do anything extra. This is useful for when you need to review the distribution and characteristics of specific nodes when using the decision tree.

Detailed node statistics table

 

What’s more, you can leverage the same drag-and-drop paradigm to quickly tune the model. In our case, you can do simple modifications like adding a new variable by simply dragging a new data item onto the canvas or more complex techniques like manually splitting or pruning a node just by clicking and selecting a node on the canvas. The whole model and visualization refreshes instantly as you make changes, and you get instant feedback on the outputs of your tuning actions, which can help drive rapid iteration and idea testing.

Governance and collaboration

A graphical and components-based approach to modeling also has the added benefits of providing a stronger level of governance and fostering collaboration. Building machine learning model is often a team sport, and the ability to share and reuse models easily can dramatically reduce the cost and effort involved in building and maintaining models.

SAS Visual Data Mining and Machine Learning enables users to build complex, enterprise-grade pipeline models that support sophisticated variable selection, feature engineering techniques, as well as model comparison processes all within a single, easy-to-understand, pipeline-based design framework.

Pipeline modeling using SAS VDMML

The graphical, pipeline-based modeling framework within SAS Visual Data Mining and Machine Learning leverages common components, supports self-documentation, and allows users to leverage a template-based approach to building and sharing machine learning models quickly.

More importantly, as a new user or team member who needs to review, tune or reuse someone else’s model, it is much easier and quicker to understand the design and intent of the various components of a pipeline model and make the needed changes.

It is much easier and quicker to understand the design and intent of the various components of a pipeline model.

Communication and storytelling

Finally, and perhaps most importantly, a graphical, low-code/no-code approach to building machine learning models makes it much easier to communicate both the intent and potential impact of the model. Figures and numbers represent facts, but narratives and stories convey emotion and build connections. The visual modeling approaches supported by SAS Viya enable you to tell compelling stories, share powerful ideas, and inspire valuable actions.

SAS Viya enables you to make changes and apply filters on the fly within its various visual modeling environments. With the model training process and model outputs all represented visually, it makes it extremely easy to discuss business scenarios, test hypotheses, and test modeling strategies and approaches, even with people without a deep machine learning background.

There is no question that a programmatic approach to building machine learning models offers the ultimate power and flexibility and enables data scientist to build the most complex and advanced machine learning models. But when it comes to speed, governance, and communications, a graphical, low-code/no-code approach to building machine learning definitely has a lot to offer.

To learn more about a low-code/no-code approach to building machine learning models using SAS Viya, check out my book Smart Data Discovery Using SAS® Viya®.

The value of a low-code/no-code approach to building machine learning models was published on SAS Users.

9月 222020
 

Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya?

SAS has been helping programmers and coders build complex machine learning models and solve complex business problems over many years.

Building on the vision and commitment to democratize analytics, SAS Viya offers multiple ways to support non-programmers and empowers people with no programming skills to get up and running quickly and build machine learning models. I touched on some of the ways this can be done via SAS Visual Analytics in my previous post on analytics for everyone with SAS Viya. In addition, SAS Viya also supports more advanced pipeline-based visual modeling via SAS Visual Data Mining and Machine Learning. The combination of these different tools within SAS Viya supporting a low-code/no-code approach to modeling makes SAS Viya an incredibly flexible and powerful analytics platform that can help drive analytics usage and adoption throughout an organization.

As analytics and machine learning become more pervasive, an analytics platform that supports a low-code/no-code approach can get more people involved, drive ongoing innovations, and ultimately accelerate digital transformation throughout an organization.

Speed

I have met my fair share of coding ninjas who blew me away with their ability to build models using keyboards with lightning speed. But when it comes to being able to quickly get an idea into a model and generate all the assessment statistics and charts, there is nothing quite like a visual approach to building machine learning models.

In SAS Viya, you can build a decision tree model literally just by dragging and dropping the relevant variables onto the canvas as shown in the animated screen flow below.

Building a machine learning model via drag and drop

In this case, we were able to quickly build a decision tree model that predicts child mortality rates around the world. Not only do we get the decision tree in all its graphics glory (on the left-hand side of the image), we also get the overall model fit measure (Average Standard Error in this case), a variable importance chart, as well as a lift chart all without having to enter a single line of code in under 5 seconds!

You also get a bunch of detailed statistical outputs, including a detailed node statistics table without having to do anything extra. This is useful for when you need to review the distribution and characteristics of specific nodes when using the decision tree.

Detailed node statistics table

 

What’s more, you can leverage the same drag-and-drop paradigm to quickly tune the model. In our case, you can do simple modifications like adding a new variable by simply dragging a new data item onto the canvas or more complex techniques like manually splitting or pruning a node just by clicking and selecting a node on the canvas. The whole model and visualization refreshes instantly as you make changes, and you get instant feedback on the outputs of your tuning actions, which can help drive rapid iteration and idea testing.

Governance and collaboration

A graphical and components-based approach to modeling also has the added benefits of providing a stronger level of governance and fostering collaboration. Building machine learning model is often a team sport, and the ability to share and reuse models easily can dramatically reduce the cost and effort involved in building and maintaining models.

SAS Visual Data Mining and Machine Learning enables users to build complex, enterprise-grade pipeline models that support sophisticated variable selection, feature engineering techniques, as well as model comparison processes all within a single, easy-to-understand, pipeline-based design framework.

Pipeline modeling using SAS VDMML

The graphical, pipeline-based modeling framework within SAS Visual Data Mining and Machine Learning leverages common components, supports self-documentation, and allows users to leverage a template-based approach to building and sharing machine learning models quickly.

More importantly, as a new user or team member who needs to review, tune or reuse someone else’s model, it is much easier and quicker to understand the design and intent of the various components of a pipeline model and make the needed changes.

It is much easier and quicker to understand the design and intent of the various components of a pipeline model.

Communication and storytelling

Finally, and perhaps most importantly, a graphical, low-code/no-code approach to building machine learning models makes it much easier to communicate both the intent and potential impact of the model. Figures and numbers represent facts, but narratives and stories convey emotion and build connections. The visual modeling approaches supported by SAS Viya enable you to tell compelling stories, share powerful ideas, and inspire valuable actions.

SAS Viya enables you to make changes and apply filters on the fly within its various visual modeling environments. With the model training process and model outputs all represented visually, it makes it extremely easy to discuss business scenarios, test hypotheses, and test modeling strategies and approaches, even with people without a deep machine learning background.

There is no question that a programmatic approach to building machine learning models offers the ultimate power and flexibility and enables data scientist to build the most complex and advanced machine learning models. But when it comes to speed, governance, and communications, a graphical, low-code/no-code approach to building machine learning definitely has a lot to offer.

To learn more about a low-code/no-code approach to building machine learning models using SAS Viya, check out my book Smart Data Discovery Using SAS® Viya®.

The value of a low-code/no-code approach to building machine learning models was published on SAS Users.

8月 252020
 

Analytics is playing an increasingly strategic role in the ongoing digital transformation of organizations today. However, to succeed and scale your digital transformation efforts, it is critical to enable analytics skills at all tiers of your organization. In a recent blog post covering 4 principles of analytics you cannot ignore, SAS COO Oliver Schabenberger articulated the importance of democratizing analytics. By scaling your analytics efforts beyond traditional data science teams and involving more people with strong business domain knowledge, you can gain more valuable insights and make more significant impacts.

SAS Viya was built from the ground up to fulfill this vision of democratizing analytics. At SAS, we believe analytics should be accessible to everyone. While SAS Viya offers tremendous support and will continue to be the tool of choice for many advanced users and programmers, it is also highly accessible for business analysts and insights team who prefer a more visual approach to analytics and insights discovery.

Self-service data management

First of all, SAS Viya makes it easy for anyone to ingest and prepare data without a single line of code. The integrated data preparation components within SAS Viya support ad-hoc, agile-oriented data management tasks where you can profile, cleanse, and join data easily and rapidly.

Automatically Generated Data Profiling Report

You can execute complex joins, create custom columns, and cleanse your data via a completely drag-and-drop interface. The automation built into SAS Viya eases the often tedious task of data profiling and data cleansing via automated data type identification and transform suggestions. In an area that can be both complex and intimidating, SAS Viya makes data management tasks easy and approachable, helping you to analyze more data and uncover more insights.

Data Join Using a Visual Interface

A visual approach supporting low-code and no-code programming

Speaking of no-code, SAS Viya’s visual approach and support extend deep into data exploration and advanced modeling. Not only can you quickly build charts such as histograms and box plots using a drag and drop interface, but you can also build complex machine learning models using algorithms such as decision trees and logistic regression on the same visual canvas.

Building a Decision Tree Model Using SAS Viya

By putting the appropriate guard rails and providing relevant and context-rich help for the user, SAS Viya empowers users to undertake data analysis using other advanced analytics techniques such as forecasting and correlation analysis. These techniques empower users to ask more complex questions and can potentially help uncover more actionable and valuable insights.

Correlation Analysis Using the Correlation Matrix within SAS Viya

Augmented analytics

Augmented analytics is an emerging area of analytics that leverages machine learning to streamline and automate the process of doing analytics and building machine learning models. SAS Viya leverages augmented analytics throughout the platform to automate various tasks. My favorite use of augmented analytics in SAS Viya, though, is the hyperparameters autotuning feature.

In machine learning, hyperparameters are parameters that you need to set before the learning processing can begin. They are only used during the training process and contribute significantly to the model training process. It can often be challenging to set the optimal hyperparameter settings, especially if you are not an experienced modeler. This is where SAS Viya can help by making building machine learning models easier for everyone one hyperparameter at a time.

Here is an example of using the SAS Viya autotuning feature to improve my decision tree model. Using the autotuning window, all I needed to do was tell SAS Viya how long I want the autotuning process to run for. It will then work its magic and determine the best hyperparameters to use, which, in this case, include the Maximum tree level and the number of Predictor bins. In most cases, you get a better model after coming back from getting a glass of water!

Hyperparameters Autotuning in SAS Viya

Under the hood, SAS Viya uses complex optimization techniques to try to find the best hyperparameter combinations to use all without you having to understand how it manages this impressive feat. I should add that hyperparameters autotuning is supported with many other algorithms in SAS Viya, and you have even more autotuning options when using it via the programmatic interface!

By leveraging a visually oriented framework and augmented analytics capabilities, SAS Viya is making analytics easier and machine learning models more accessible for everyone within an organization. For more on how SAS Viya enables everyone to ask more complex questions and uncover more valuable insights, check out my book Smart Data Discovery Using SAS® Viya®.

Analytics for everyone with SAS Viya was published on SAS Users.

11月 122019
 

US military veterans are mission-focused, team-oriented and natural leaders that benefit any organization that hires them. SAS has many programs to help US military veterans find jobs within the company and elsewhere. SAS also works with veterans organizations to use their data to help transitioning military members and their spouses [...]

Analytics helping veterans make the transition to civilian life was published on SAS Voices by Trent Smith

8月 052019
 

As a company, SAS consistently supports #data4good initiatives designed to help those less fortunate around the world. SAS Press team members recently took some time to reflect on the SAS initiatives that inspired them. We thought this would be a good opportunity to introduce some of the team who work so hard on our SAS Press books.

Sian Roberts, Publisher

I lead the SAS Press team and oversee the publication of our books from start to finish, including manuscript acquisition, book development, production, sales, and promotion.

Having lost both my dad and grandmother to cancer, the work SAS is doing to help improve care for cancer patients by tailoring treatments for individuals particularly resonates with me. For example, the wonderful work that is being done with Amsterdam University Medical Center to use computer vision and predictive analytics to improve care for cancer patients is of particular interest to me. My hope is that by using analytics and AI on data gathered from hospitals, research institutes, pharma and biotech companies, patterns can be identified earlier, and survival rates will increase.

 
 

Suzanne Morgen, Developmental Editor

I work with authors to help them develop and write their books, then go to conferences to sell those books and recruit more authors!

At SAS Global Forum, we heard about a pilot program at the New Hanover County Department of Social Services that uses SAS to alert caseworkers to risks for children in their care. I have been a foster parent for several years, so I am excited about any new resources that would help social workers intervene earlier in kids’ lives and hopefully keep them safer and even reduce the need for foster care. I hope SAS is able to partner with many more social services departments and use analytics to help protect more kids in the state and across the country.

Emily Scheviak-Livesay, Senior Business Operations Specialist

As a SAS employee for 22 years, I have learned to wear many hats. At SAS Press, I keep the business running smoothly and manage the metadata of all our books in all formats. I also work with our partners to ensure our titles are available both in the US and globally.

I love this story about JMP working with the Animal Humane Society! I’m a huge fan of “adopt, don’t shop” and it makes me so proud to work at a company where one of our products was used to assist in furthering the cause. For JMP to be able to take a huge amount of data from various sources and turn it into valuable information for The Animal Humane Society is amazing! Helping to care and save animals is what it’s all about. It truly is a fairy “tail” ending.

 

Missy Hannah, Senior Associate Developmental Editor

I work directly with SAS Press and JMP authors to plan and implement marketing strategies for our books. I grew up with a mother who not only was a Systems Engineer but who taught me all about technology. Looking back, I was always watching her code and work with technology and IT my entire life and seeing her do this meant those things came very easily for me. But often, other young women don’t find mentors in the field of data analytics and technology. Data shows that women account for less than 20% of computer science degrees in the U.S. and hold less than 25% of STEM-related jobs. That is why the Women’s In Tech Network at SAS has been something I have really enjoyed having at my company. SAS creating the Women’s Initiative Network (WIN) and all the other work they are doing to increase women in STEM and data fields is something that really matters.

Catherine Connolly, Developmental Editor

I work with authors to develop books that support SAS’ business initiatives. My main areas of focus are JMP, data management, and IoT.

There are so many SAS initiatives through #data4good that make me proud to be a SAS employee. One initiative I read earlier this year that stuck with me was a partnership between SAS and CAP Science to combat against repeated domestic violence. CAP Science developed wearables to be worn by both the domestic violence victim and the offender. The wearable uses SAS software to continuously collect data and report on the offender’s location in real-time in an effort to stop future attacks.

 
 

We hope you enjoyed this small insight into some of our team. We are all very proud to work for a company that takes the time to improve the lives of those who need it and uses the power of data and analytics to help the world.

What SAS #data4good initiative has been your favorite? Make sure to comment below!

What really matters: SAS #data4good and the SAS Press team was published on SAS Users.

8月 052019
 

As a company, SAS consistently supports #data4good initiatives designed to help those less fortunate around the world. SAS Press team members recently took some time to reflect on the SAS initiatives that inspired them. We thought this would be a good opportunity to introduce some of the team who work so hard on our SAS Press books.

Sian Roberts, Publisher

I lead the SAS Press team and oversee the publication of our books from start to finish, including manuscript acquisition, book development, production, sales, and promotion.

Having lost both my dad and grandmother to cancer, the work SAS is doing to help improve care for cancer patients by tailoring treatments for individuals particularly resonates with me. For example, the wonderful work that is being done with Amsterdam University Medical Center to use computer vision and predictive analytics to improve care for cancer patients is of particular interest to me. My hope is that by using analytics and AI on data gathered from hospitals, research institutes, pharma and biotech companies, patterns can be identified earlier, and survival rates will increase.

 
 

Suzanne Morgen, Developmental Editor

I work with authors to help them develop and write their books, then go to conferences to sell those books and recruit more authors!

At SAS Global Forum, we heard about a pilot program at the New Hanover County Department of Social Services that uses SAS to alert caseworkers to risks for children in their care. I have been a foster parent for several years, so I am excited about any new resources that would help social workers intervene earlier in kids’ lives and hopefully keep them safer and even reduce the need for foster care. I hope SAS is able to partner with many more social services departments and use analytics to help protect more kids in the state and across the country.

Emily Scheviak-Livesay, Senior Business Operations Specialist

As a SAS employee for 22 years, I have learned to wear many hats. At SAS Press, I keep the business running smoothly and manage the metadata of all our books in all formats. I also work with our partners to ensure our titles are available both in the US and globally.

I love this story about JMP working with the Animal Humane Society! I’m a huge fan of “adopt, don’t shop” and it makes me so proud to work at a company where one of our products was used to assist in furthering the cause. For JMP to be able to take a huge amount of data from various sources and turn it into valuable information for The Animal Humane Society is amazing! Helping to care and save animals is what it’s all about. It truly is a fairy “tail” ending.

 

Missy Hannah, Senior Associate Developmental Editor

I work directly with SAS Press and JMP authors to plan and implement marketing strategies for our books. I grew up with a mother who not only was a Systems Engineer but who taught me all about technology. Looking back, I was always watching her code and work with technology and IT my entire life and seeing her do this meant those things came very easily for me. But often, other young women don’t find mentors in the field of data analytics and technology. Data shows that women account for less than 20% of computer science degrees in the U.S. and hold less than 25% of STEM-related jobs. That is why the Women’s In Tech Network at SAS has been something I have really enjoyed having at my company. SAS creating the Women’s Initiative Network (WIN) and all the other work they are doing to increase women in STEM and data fields is something that really matters.

Catherine Connolly, Developmental Editor

I work with authors to develop books that support SAS’ business initiatives. My main areas of focus are JMP, data management, and IoT.

There are so many SAS initiatives through #data4good that make me proud to be a SAS employee. One initiative I read earlier this year that stuck with me was a partnership between SAS and CAP Science to combat against repeated domestic violence. CAP Science developed wearables to be worn by both the domestic violence victim and the offender. The wearable uses SAS software to continuously collect data and report on the offender’s location in real-time in an effort to stop future attacks.

 
 

We hope you enjoyed this small insight into some of our team. We are all very proud to work for a company that takes the time to improve the lives of those who need it and uses the power of data and analytics to help the world.

What SAS #data4good initiative has been your favorite? Make sure to comment below!

What really matters: SAS #data4good and the SAS Press team was published on SAS Users.

4月 292019
 

A persistent analytics talent gap creates big opportunities for people who can wield analytics to help organizations make better decisions. Innovative analytics users and students who are rushing to fill that gap, and those who teach them, are being honored this week at SAS Global Forum. A special Sunday event [...]

SAS celebrates analytics talent, and those who shape it was published on SAS Voices by Trent Smith

2月 132019
 

SAS has worked with our exam delivery partners to integrate a live lab into an exam, which can be delivered anywhere, anytime, on-demand.

The post New Performance-Based Certification: Write SAS Code During Your Exam appeared first on SAS Learning Post.