For the second year in a row, a crowd of students, corporate data scientists, academicians, and entrepreneurs gathered in Dallas to explore the world of machine learning and data science. The theme of “Technology. Innovation. Career.” was carried throughout the event with speakers focusing on a variety of topics from advanced analytics projects to tooling and APIs. Both professors and professionals gave insight into what’s new in data science, how business and education must change to keep up, and where the tech is going over the next few years.
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
Students line up to learn from benefactors
Sponsors like Data Application Lab had a particular interest in being present at the data science event. According to Celine Lin, VP of Business Development, “Many of these students are looking for ways to build a strong foundation in data science. Our company has a large consulting component, but one of the most important things we do is provide education focused on helping students get started in their careers.”
Available courses at the Lab address a variety of areas from data wrangling to algorithms, Big Data engineering, Natural Language Processing, business analysis, and more. Committee Chair Jason Geng’s presentation on the “Data Science Project Lifecycle” highlighted the importance of these various skill sets. Machine learning might seem more glamorous than data management, but neither one can be seen as more essential than the other.
With an attendee base featuring a significant student cohort, the panel discussion and many of the Q&A sessions focused on how to start or transition to a career in this rapidly expanding field. For example, one attendee asked about making the switch from business administration to data science. Although these two disciplines might seem worlds apart, the reality is that the boundaries are starting to blur. Dr. Meltem Ballan, Senior Consultant in Advanced Analytics at Clarity Solution Group, spoke about this erosion and what it has been like to work with people from many different backgrounds who now touch the data science world. “Often you are sitting in a room with data scientists or analytics people with no business background and sitting with a business owner or VP of Marketing to discuss the data.”
In fact, bringing good business sense to the table is essential for successful big data and analytics initiatives to succeed. The conference’s first speaker, Juan Vasquez, Data and Engagement Strategist in the LA Mayor’s Operations Innovation team, was not a technology whiz. Instead, he brought his expertise in communication and a background in marketing to bear on the city’s challenge with digitally managing its real estate. His skill set proved essential in helping city officials and decision makers understand the value of the project and how to use the resulting solution to make better decisions.
Someone with a master’s in business might well find themselves starting at the periphery of a data science project, learning more about the discipline as they go, and finally embedding themselves in the team as an expert in a particular area. Just as machine learning involves self-evolution, students will find that the field of data science is changing too rapidly for the stately pace of academia to keep up. They must be willing to pursue knowledge and explore new boundaries on a continuous basis in the ever-shifting environment of the business world as well.
Students still need to work on networking
The conference did provide access for students to speak directly with experts and gain wise counsel about their own careers. But by the time the final talk was finished, the auditorium was sparsely populated. Digital Media Consultant Gabe Bautista found that far too few attendees took advantage of what may have been one of the most valuable aspects of the conference—the opportunity to network at the end of the day. “It’s not just about the knowledge, it’s the people.”
This is a lesson that all students of Data Science must grapple with in order to launch the careers they want. While this field has plenty of room to accommodate an influx of fresh talent, matching the right people with the right projects will require more than computations and analytics. For a bright future in a value-driven business world, these budding data scientists will have to make connections that are more than digital.