ABOUT US
Established in 2015, The Data Science Conference® provides an exclusive space where analytics professionals can network without being prospected by other attendees. This conference is designed strictly by professionals, for professionals.
We provide an environment where the material presented and discussed is substantial, technical, and directly relevant to the data science practitioner. Our participants do not use the event to conduct commercial business; instead, they interact as scientists in an atmosphere where every attendee is treated with decency, dignity, and respect. This is a conference, not an expo.
To maintain this standard, no presentation time is allotted to sales pitches. There are no sponsor booths, no paid advertising, and we will never recruit you or sell your information. Furthermore, our complete agenda and speaker list are open to the public with no email required to view them. Presentation slides will also be provided at no extra cost (for those sessions our presenters agree to share).
We believe in fair pricing. Conferences that accept corporate sponsorship often cover most, if not all, of their operational expenses before selling their first ticket. Yet, once at the venue, attendees are forced to sit through sponsored sales presentations. In effect, the true cost is passed down to the attendees, who pay top dollar to sit through already paid-for sales pitches. We do not overcharge you.
As data scientists, we understand that vendors and recruiters are a necessary part of our craft. Providing a vendor- and recruiter-free conference does not mean we do not recognize their importance to the field. However, because there was not a single vendor- and recruiter-free event dedicated to predictive analytics, data mining, machine learning, or data science, we felt a critical gap existed. This conference was built to fill that gap.
We welcome you to The Data Science Conference®! This is your space. Get involved, get in touch, and let us know what you think. We will listen.
HOW IT ALL STARTED!
On an early spring day in 2015, I was attending an analytics conference with a ticket price roughly double what The Data Science Conference® charges today. It followed the standard industry template—sponsor booths lining the hallways, rows of seating, corporate brochures, and ubiquitous name tags. However, the first three presentations consisted entirely of vendor promotions for specific products and services. Intrigued, I began marking each session on my agenda with a ‘P’ for technical practitioner or a ‘V’ for commercial vendor. By the conclusion of the two-day event, an astonishing 65% of the presentations were classified as vendor sales pitches. I found myself asking, “If any of these sales representatives contacted me at my corporate office, I would immediately route them through our standard procurement and vendor evaluation channels. Why am I utilizing corporate development funds to sit through content that offers zero professional development?”
Exhausted by the continuous sales pitches, I left the session rooms to find a coffee, only to discover that refreshments were strictly confined to the breakfast hour. It seemed reasonable to expect continuous amenities given the premium registration fee. On my way to a café outside the hotel, I was actively pursued across the lobby by a vendor—or perhaps a recruiter—demanding my professional contact information.
Later that evening during the networking reception, a casual professional conversation with a peer rapidly shifted into an aggressive pitch for their corporate services. Looking past the vendor, I managed to spot one of the few authentic practitioners on the agenda, who had presented an excellent session on fraud detection. At the time, in addition to my primary corporate role as a senior statistician for the largest automotive insurer in the United States, I served as an adjunct professor for a Data Science Master's program in Chicago. The department chair had recently asked if I could recommend an active fraud detection expert to design and instruct a new graduate course. I approached the speaker, introduced myself, and we began discussing the curriculum opportunity. Suddenly, an aggressive sales representative walked directly between us, standing uncomfortably close. When we politely asked if she required assistance, she replied, "No, I am just waiting for you to finish so I can present our corporate services to you." Despite the visible awkwardness and my subsequent exit from the conversation, she remained completely undeterred. Genuine professional networking was virtually impossible.
I returned to my hotel room deeply unsettled. Coming from an academic background in physics, I was accustomed to scientific conferences where peers meet to debate pure science rather than pitch commercial products to one another. I recognized that the data science community deserved that same rigorous, objective environment. That night, I resolved to establish The Data Science Conference®.
Some years later, the event had expanded to a scale that made it impossible to balance both the organization and my corporate career. I chose to step away from my corporate role to concentrate entirely on managing and expanding the conference.
I extend my sincere gratitude to our dedicated community of attendees, many of whom continue to support our mission year after year, traveling across the country to join us at each new assembly.
THE CONFERENCE CHAIR
Dr. Michael Tsiappoutas is the Founder and Conference Chair of The Data Science Conference® (TDSC). An applied physicist by training, Dr. Tsiappoutas has been an active practitioner in the insurance industry since 2007, specializing in property and casualty (P&C) automotive insurance and Fortune 100 healthcare advanced analytics. His core industry expertise spans actuarial data science, predictive modeling, machine learning, and statistical learning.
Throughout his corporate career, he has leveraged his engineering and mathematical background to design innovative technical solutions. His project portfolio includes building advanced text analytics platforms to predict corporate litigation trends, as well as developing advanced image processing models to identify fraudulent mechanics shops.
In addition to his corporate work, Dr. Tsiappoutas has extensive experience in academia. He has conducted specialized research in statistical digital signal processing focused on early 20th-century voice acoustics, provided statistical consulting, and taught mathematics and physics at various community colleges and universities. In the graduate space, he served on the Advisory Board for Lewis University's Master’s in Data Science program, where he developed and instructed the core Mathematics for Data Scientists curriculum. He has also instructed advanced graduate coursework for Southern New Hampshire University's Master’s in Data Analytics program.
Dr. Tsiappoutas holds a Ph.D. in Engineering & Applied Physics, a Master of Science in Applied Physics, a Master of Arts in Quantitative Psychology, and a double-major Bachelor’s degree in Psychology and Physics. Since 2008, he has served as the CEO of NeoNorm, LLC, the independent organizing entity that established and manages The Data Science Conference® (launched in 2015). He resides in Connecticut with his wife and son.