In this two-hour session, renowned Data Scientist, Dr. Murthy Kolluru, will demystify Data Science and provide business leaders with a deeper understanding of the subject. The emphasis will be on the intuition, not the math, behind cutting edge algorithms and concepts like Machine Learning. We will also discuss the ROI of data driven decision making and highlight a few business problems that Data Science enabled us to solve.
Dr. Murthy holds a Ph.D. in Materials Science from Carnegie Mellon and started his career as a Rocket Scientist. He has architected and solved extremely complex problems in both business and engineering and is considered one of the top Data Science academicians in the world.
Gaurav is a dynamic entrepreneur and co-founder of Soothsayer. He holds Masters Degrees in Electronics and Technology and has spent over two decades in technology. Prior to Soothsayer, he founded two successful companies and oversaw the delivery of enterprise solutions to Fortune500 companies in the US, UK, Germany, and Asia-Pacific. Gaurav oversees human resources, operations and project delivery.
Chris is a co-founder of Soothsayer. He studied Political Science at The Ohio State University, but earned his stripes in business. He brought the Soothsayer team together and shepherds it’s strategic vision. Chris oversees marketing, partnerships, sales, and strategy.
Dr. Gnana combines analytics and synthetics to diagnose organizational problems and design appropriate analytic approaches. Gnana has holds a PhD from the University of Pennsylvania, where he still does research. He has 15+ years of commercial and research based analytics consulting experience. His primary work centers on complex system problems in business and society. This involves systems (design) thinking, analytics (modeling, simulation, and analysis), emphasizing human behavior, representing rich interconnections between parts, and employing both quantitative and qualitative knowledge elicitation techniques. He publishes regularly in reputed journals.
Akshay was the first Data Scientist at Soothsayer, and he is actively involved in problem assessment, solution design, and project execution. He holds a Bachelor’s in Control Systems and a Masters in Computer Science from The Ohio State University. Prior to joining Soothsayer, he was a Senior Consultant at IBM, where he successfully delivered on multiple advanced analytics projects. He is an expert in Machine Learning, especially in relation to Speech Recognition and the Rapid Development of Pronunciation Models for low resource languages. He enjoys tackling complex business problems and building world-class solutions for clients.
Prashanth is involved in Data Science consulting and business development for Soothsayer’s education initiative. He holds a Bachelor’s in Metallurgical Engineering and a Master’s in Business Analytics from Drexel University. Prior to joining Soothsayer, he worked as a Data Scientist on an analytics product development team. He also worked for the International School of Engineering. He has a strong background in Data Mining, Optimization, and Machine Learning.
Jarred holds a Bachelor’s degree from the University of Arizona in Applied Mathematics and Physics, as well as a Master’s in Physics from The Ohio State University. Prior to joining Soothsayer, he worked in multiple fields ranging from condensed matter physics to quantitative psychology in physics education. This included developing models for electronic transport across molecular junctions to assessing the conceptual knowledge state of students in various levels of physics. He has a strong background in analytical thinking and coding, as well as the ability to apply these techniques across a wide spectrum of problems.
Andrea is actively involved in relationship building, strategy, and corporate training. She studied Game Theory, Intelligence Analysis, and Political Science at The Ohio State University. She leverages this background to develop a deep understanding of client problems and offer actionable solutions.
Dr. Murthy is a former Rocket Scientist and the President of the International School of Engineering, which was recently named the 3rd best Data Science program in the world (www.CIO.com).
Dr. Boorn has spent the bulk of his career on Urban Planning, Sustainability, and issues that arise from an urbanized environment. We work together to solve problems related to the vast and disconnected dimensions of urban places.
Dr. Li is trained in science and educated in management. He has multiple degrees (including an MBA) and certifications. He is the former Head of Predictive Analytics at Alliance Data, and he advises us on analytics projects related to retail.
David was most recently the VP of Analytics at Battelle, after years with Bell Labs applying research in data management and analysis. He advises us on projects related to Telecommunications.
Ryan is an industry thought leader and frequent speaker at major conferences. He is currently an EVP at one of the largest healthcare marketing agencies in the world, and he advises us on projects related to Marketing Analytics.
Paul is a Professor at the University of Cincinnati, a Statistician, a veteran Sports Journalist, and the founder of PredictionMachine. We are working together to develop a best in class analytical solution for the NFL.
Zain was an analytical leader at JP Morgan Chase and Wells Fargo. He is now the Founding Executive Director of Bellarmine University’s MS in Data Science. He advices us on analytical innovation in Banking.
Matthew is an experienced engineer and human resources executive. He advises us on opportunities for leveraging data analysis to drive practical and profitable human capital decisions.
Data Science Demystified for Decision makers - Nov 2014. Series of Seminars were conducted in Columbus, OH & Detroit, MI.
We are not limited by canned tools purporting to do “Analytics” but really just querying your data. We are experienced working with data of all shapes, sizes, and complexity. Every solution is architected by a PhD, and we leverage our team of Mentors on problems that require specific domain knowledge.
For companies looking to develop a new product or to improve upon an existing one, we can help. We are currently exploring multiple product development partnerships and plan on developing state-of-the-art Data Science solutions and Intellectual Property for a variety of domains.
Our corporate training programs are led by world-class PhD’s from schools like Carnegie Mellon, Johns Hopkins, Stanford, and Wharton. Each program is custom and has the option of producing a deliverable at the end. We can deliver on a project while training your team to solve problems in the future. We offer both executive and hands-on training programs.
Traditional algorithms fail to reveal well concealed patterns in complex, high dimensional, and unstructured data. For this kind of data, methods like decision trees tend to extract patterns that are obvious, meaning patterns with 70% or more support. This results in very few eye openers.
We utilize advanced Machine Learning techniques to search through data and extract Hidden Insights and Micro-patterns that most off-the-shelf methods miss. Instead of just drawing lines down the middle and computing averages, we identify all data points and their surrounding area, and extract actionable and intuition crushing insights.
When needed, we can extract actionable If-Then patterns from otherwise seemingly black-box results. This means you can achieve the accuracy of the complex models, without sacrificing the explicability of traditional methods.
It is becoming increasingly important for businesses to tap into the potential of unstructured data. We are experienced with working on complex problems involving images, speech, and text.
We customize solutions that use both syntactic and semantic analysis, and our delivery team has successfully executed several projects involving Natural Language Processing, Sentiment Mining, and Text Classification.
We actively work with the latest and most sophisticated Machine Learning techniques to deliver these solutions. Our aim is to solve the unstructured data problems of the present and to continue exploring solutions for the unstructured data of the future.