Zencos Team Headed to Denver for 2018 SAS Global Forum

Pre-Conference, Extra FeeWorkshop by Tricia Aanderud and Jaime D’Agord

Tag: Data Visualization, Data Storytelling, Presenting Data

What happens when we think like a storyteller? One of the hardest parts of data is capturing people’s attention. Numbers may prove your point, but how do you get others to care about them? Data storytelling allows you to mix interesting narratives with data so that you can maximize your impact. This is accomplished through a mixture of data visualization and storytelling theories and best practices. Whether you’re an analyst crunching numbers or a manager needing to communicate in a data-driven way this session introduces the skills needed to create effective data stories. In this workshop, we review data storytelling methods, learn what stories motivate which audiences, learn how to best display data, and create messages that resonate. The topics are focused on general best practices and features techniques using SASΠVisual Analytics.

Note: Register for the workshop when you register for the conference.

Table Talk presented byBen Murphy

How can you build a superstar data science team to help your organization find insights and work smarter? The ability to define and answer fundamental questions vital to the formation of an analytics team can help organizations be more successful at creating a data-driven culture. Using objective evidence to answer questions and generate actionable solutions enables any organization to improve how they do business. Building a team to help drive those insights from the ground up forms a sustainable foundation for success. In this table talk, we discuss defining the right team, finding the appropriate team members, identifying projects that provide value, and building momentum for long-term success.

Breakout session presented by Tricia Aanderud

Tag: Data Visualization, Location Analytics, SAS Visual Analytics, SAS Viya

Each year, more companies invest in adopting and integrating machine learning into their operations. One common challenge organizations face is identifying appropriate machine learning techniques that can generate actionable insights. There are many machine learning methods, but not all are suitable and applicable to every business problem. This super demo connects the needs of modern enterprises with appropriate industry-agnostic machine learning techniques. These machines learning techniques are explained at a high level with realistic industry examples showing how to interpret the results, what questions can be answered for different industries and how data-driven decision making can shape the direction of the organization.

Breakout session presented by Grace Lybrand and Michael Swanson, Boston Scientific

Tag: Text Analytics, SAS Viya

Data scientists are trained in analyzing data, specifically unstructured text data. Often, they need to mine insights from unstructured text data in unfamiliar industries. This paper articulates the lessons we learned as we analyzed data and successfully partnered with subject matter experts to inform and guide our dive into the medical device industry. Using SASΠViyaά we text-mined the narratives submitted with medical device failure reports to the US Food and Drug Administration (FDA). These reports are available in the Manufacturer and User Facility Device Experience (MAUDE) database. As you embark on your next text analytics project, use our lessons learned to strengthen your partnerships and insights.

E-Poster presented by Jaime D’Agord

The goal of visualizing data is to communicate information effectively, to provide decision-makers a quick and easy way to analyze data, and to help your readers understand data. Doing this might seem as simple as putting data into a graph. However, there’s more to it. Your color choices can make or break a visualization. It is not just an aesthetic choice, it’s a crucial tool to convey information. When used correctly, color sets the tone and helps to create visualizations that tell stories. On the contrary, a badly chosen color palette obscures the information you are trying to portray and, in turn, makes the data visualization less effective. In this poster, we explore color choices using SASΠVisual Analytics 8.1 running on SASΠViyaή

Breakout session presented by Tricia Aanderud and Scott Leslie

The recent versions of SASΠVisual Analytics include enhancements to several useful features that can elevate your reports and dashboards from good to great. This presentation describes workable solutions to common obstacles faced by report developers and data scientists. Topics include the use of parameters, when and how to calculate items, adding dynamic chart titles, and creating hierarchies to add drill-down functionality. This presentation is suitable for users of all experience levels and demonstrates how to optimize SAS Visual Analytics to elevate your reports and dashboards to the next level.

Partner Super Demo presented by Grace Heyne Lybrand

Tag: SAS Viya, Machine Learning

Does this years NCAA Basketball Champion stack up to those of the past? What’s the deal with my college teams losing streak? Do free throws matter? How do my fellow fans feel about the tournament results? Yes, this is the annual dialog of March Madness. There are an estimated 50,000 public web APIs that connect us to the data but not to the answers. When digging for the answers to these questions and more, SAS΍ ViyaΠprovides a unified environment to combine the flexibility of Python with the power of SASήPython is the ideal link between the data and the insights it contains and the analytical power of SAS Viya. In this demo, we showcase this power, while getting to the bottom of pressing March Madness questions. To do this, we use the Python SWAT package within Jupyter Notebook to collect data from an API, load the data to SASΠCloud Analytic Services, and analyze it using SASΠVisual Data Mining and Machine Learning, and SASΠVisual Analytics on SAS Viya.

Breakout session presented by Ben Murphy

Tag: Managing with Data

Each month it seems that a new technology introduced that can transform your organization in unimaginable ways. Each technology arrives with its own set of industry buzzwords that make it difficult to understand how your organization would benefit. In this talk, we answer questions we are asked most frequently about machine learning, data science, artificial intelligence, and advanced analytics. We review the relative strengths and weaknesses of the various tools, techniques, and technologies associated with these buzzwords. You will walk away a winner by knowing how to put each to its best use.

E-Poster presented by Jaime D’Agord

Tag: Data Visualization, Data Storytelling, Presenting Data

Let’s face it the media has given sharks a bad rap, portraying them as villains and creating a culture that fears the fin. Sources like the International Shark Attack File, the Global Shark Attack File, and OCEARCH provide us with staggering amounts of data every day. This data gives us the ability to map shark attack occurrences all over the world, to determine the activity that brought on the attack, whether the attack was provoked, and, ultimately whether it was a fatal attack. We can use the data to tell data stories about this animal species that fascinates so many of us. So, the prevailing question is, can we reduce fear over shark attacks using data? We explore this possibility using SASΠVisual Analytics 8.1 on SASΠViyaή

Breakout session presented by Sean Aakenbruck and Grace Lybrand

The introduction of SASΠViyaΠopened SASΠand its industry-leading machine learning algorithms to the open-source community. With the ability to connect open-source interfaces such as Lua, Python, and R, as well as REST APIs to SASΠCloud Analytic Services (CAS), this new engine becomes a powerful tool to include in any analytical toolbox. UsingCAS actions, users can easily surface data and analytical model results in a WebApp through REST API connectivity. This capability is one key aspect that SAS Viya offers to provide real-time analytics. REST APIs provide a way to access SAS Viya over HTTP, and CAS capabilities are more efficient in communicating between front-end and back-end applications. Zencos will showcase leveraging the capabilities of the CAS Server and connecting to REST APIs to surface data for real-time decision making using a case study.

Breakout session presented by Ivan Gomez

Scalability. Fault tolerance. Load balancing. High performance. High Availability. These are all phrases that commonly show up during analytics infrastructure conversations. However, maybe you are still confused about how all this relates to SASΠGrid Manager and what it would mean for your organization. When considering SAS Grid Manager, many customers have similar points of confusion: How will their SASΠworkflow change? How do they connect to the grid? How should they manage it, and what do they need to do in order to successfully implement it? In this paper, we cover questions we are asked most often about implementation, administration, and usage of SAS Grid Manager.

Breakout session presented by Leigh-Ann Herhold

With the new SASΠVisual Analytics and SASΠVisual Data Mining and Machine Learning capabilities on SASΠViyaά institutions can more quickly and easily understand drivers of customer risk in a visually appealing way. Analysts can identify new opportunities for stakeholders to identify potential gains or losses by understanding drivers of risk and detecting new risk factors as they emerge over time. Using a combination of supervised and unsupervised machine learning methods, businesses can further understand and improve their own definition of customer risk as an organization. In this talk, Zencos shares its expertise in identifying high-risk attributes, understanding the relative importance of each driver, and recognizing key combinations of factors associated with risky behavior through an anti-money laundering case study.

Breakout session presented by Leigh-Ann Herhold and Ivan Gomez

Ever wonder how the algorithms used by Facebook and Google detect your friends in your photos? Image recognition and classification algorithms, such as deep neural networks, can extract important information from photos and classify them almost instantly after you post a picture. Not only is this process useful on social media, but there are numerous applications of image classification algorithms in healthcare, manufacturing, and security screenings. Using the latest machine learning capabilities available in SAS΍ ViyaΠfor text and image processing, organizations can leverage in-memory processing with SASΠCloud Analytic Services (CAS) and enhanced parameter tuning to develop more sophisticated deep learning models. In this paper, we discuss key components of building an image classification algorithm.