Elder Research

Happy Pi Day! This is our favorite way to celebrate. ?? ?? #PiDay

????%—that’s the percentage of use cases most analytics applications can handle. ?????? ???????? ?????????? ???????? ???????????? ??%? At business scale, those data anomalies can translate to millions or billions of elements that may be tied to other datasets. And that poses a big challenge, says Data Scientist Tom Shafer: “This mixing of regular and irregular data can be a serious problem for machine learning or AI models that only expect to process typical data.” ?????? ????????’?? ?????? ????????????: “Sometimes anomalous data don’t stem from data-entry errors, storage corruption, or faulty manipulations. When anomalies correspond to fraudulent or otherwise unethical actions, worrisome sensor readings, or physically impossible entries, more value might come from identifying and remedying anomalies than from modeling the normal cases.” Want to uncover valuable insights in your data? ???????? ??????’?? ???????????? ???????? for tips on using anomaly detection effectively, and find out what he says is key to matching the right solution to your business challenges: https://lnkd.in/ehuv9JwM #DataScience #DataAnalytics #AnomalyDetection

Considering a leap from academia into data science? The math can add up to a rewarding career—just ask Scott Atkinson. He’s sharing his journey from pure mathematics to data science in this series. Follow along for insights. It’s never too late to make a shift! ↗?

If you're building a time series forecasting model and you'd like to test machine learning algorithms, I encourage you to give MLForecast a try: https://lnkd.in/eWiRMk9D This week, the Commercial team at Elder Research had our monthly Datathon! This is a time when our team gathers to explore interesting data, try out new techniques, or (as in this week's case) try to win a friendly competition by building a model with the best performance. This month's topic was selected by Matt Bezdek and our objective was to forecast crop production for 14 different crops over 6 different countries. I leveraged the MLForecast framework to develop a couple of models, and the capability I find especially handy is how you can specify feature and target transformations directly in the model object. For example, here's how I specified the number of lags I wanted in my model, the lag transformations I wanted to apply, and the target transformation I wanted to apply: model = MLForecast( ???? models={ ???????? 'lgbm': lgb.LGBMRegressor(), ???? }, ???? freq='YS', ???? target_transforms=[Difference([1])], ???? lags=[i for i in range(1, 11)], ???? lag_transforms={ ???????? 1: [ ???????????? RollingMin(window_size=4), ???????????? RollingMean(window_size=4), ???????????? RollingMax(window_size=4), ???????????? RollingStd(window_size=4) ???????????? ] ???? }, ) I encourage you to check it out the next time you need to build a machine learning model for time series forecasting! P.S. Shout out to Paige Spell for winning the competition!

Our founder, John Elder, shares the painful truth about all analytics projects: “Every stage of an analytics challenge is susceptible to error and misdirection, seeping in to weaken or destroy useful results.” So how do protect models from these hidden pitfalls? ?? ???????? ???????? ???? ?????????? ???????? ???? ????????: “It takes expertise and discipline—responsible AI practices—to guard against these hazards.” In his new blog, John focuses on one key hazard that responsible AI helps address: outlier data points. ?? Check out the blog for real-world examples illustrating how outliers can impact projects—and some key lessons we can learn: https://lnkd.in/eDCSKEjt #DataScience #DataAnalytics #ResponsibleAI

At Elder Research, we combine innovation and purpose to solve some of the toughest challenges in national security and defense. If you have a ???? ???? ???? ???????? ?????????????????? and want to join a team that thrives on collaboration, curiosity, and making a measurable impact, come connect with us! ???? ????????????????, ?????????? ??, our recruiting team would love to meet you at the ClearedJobs.Net career fair in Herndon, VA. ????’???? ???????????? ?????? ?????????? ?????????????? ??????????: ? Data Scientist ? Data Engineer ? Data Analyst ? Software Engineer ? R&D Computer Vision or LLM Machine Learning Engineer ?????????? ???????? ?????????? ???????? ????????: elderresearch.com/careers ?? ?????? ???????? ?????????? ????????????????? ?? Work alongside passionate colleagues driven by curiosity and innovation ?? Contribute to impactful projects supporting the intelligence community ?? Thrive in a collaborative, supportive environment ???????????????? ????????: https://lnkd.in/eUCPQRyc We’re ready to connect and discuss how you can grow your career at Elder Research. #ClearedJobs #IntelligenceCommunity #DataScience #DataAnalytics #DataScienceJobs

Think a liberal arts degree means you can’t break into data science? Think again. ?? Henry Mead made the leap—and in this blog, he shares how. “The key skills aren’t necessarily technical prowess from the start but rather diligence, curiosity, and an open mind,” says Henry. If you’re considering a data career, check out the blog for practical tips to help you succeed. ?? “The field is expanding rapidly, and the entry requirements aren’t as daunting as you might think,” says Henry. It’s about curiosity, problem-solving, and persistence. ?? “Be ready to dedicate not just time but effort.” ???????? ?????? ???? ??????????’?? ???????? ????????: https://lnkd.in/e52hAamm

AI can accelerate innovation, but as Jericho McLeod, PhD points out, it's up to us to guide it in the right direction.

What’s the key to serving well? It starts with listening. ?? Last Friday, our team took a deep dive into how we can better serve our government partners, thanks to an insightful training with our friends at The American Small Business Coalition (The ASBC?). ?? From discussing ways to assess data analytics needs to hands-on experience with research tools, we honed our ability to listen, ask the right questions, and align our work with agency priorities. The icing on the (cup)cake ?? was celebrating the birthdays of our government team leader, Shree Whitaker Taylor, PhD, Client Engagement Manager Jonathan Ericksen, and Data Scientist Daniel Grogan. A huge thank you to Guy Timberlake and The ASBC team for equipping the federal contracting community with the knowledge and tools to make a bigger impact! ↗? #GovCon #FederalContracting #DataAnalytics #OnwardandUpward

Generative AI can create, assist, and innovate. ↗? But it also comes with risks. ?????? ???????? ????????? When we understand and address those risks, we can build AI that delivers even greater value. Here’s what Principal Scientist Michael Thurber has to say: “Safety is first. This is a new world. It’s very different than the one before it.” As AI continues shaping our world, there are plenty of opportunities for organizations to lead responsibly. It starts with being proactive, understanding the risks, and getting everyone on the same page. ?? Our Responsible AI Framework is a great starting point for those conversations. In it you’ll find some key questions to assess AI risks and guide solutions toward trust, safety, and meaningful impact. Explore the framework today at elderresearch.com/rai. #ResponsibleAI #ArtificialIntelligence #GenerativeAI #GenAI #AIInnovation #DataScience