Data science can be used to discover patterns, develop algorithms for handling massive amounts of data quickly, and to enhance the security of sensitive data while supporting data-driven decision-making. Knowing how to interpret data, the lingo required to navigate it, and how to use data science to generate a positive influence can be invaluable skills in today’s business environment.
Here’s Part II of our Q&A with Tallan Director of Data Science and AI Lee Harper, where we’ll assess the key components of data science. Click here for Part I, an introduction to our practice.
What is the future of data science?
Data science is a field that is evolving exceptionally quickly – work that was done even 2-3 years ago is often out of date today, so predicting the future is hard (even though it’s basically what we do day-to-day). Aside from the increased adoption of data science itself, here are some specific emerging trends in the field:
This solves one of the biggest challenges in enterprise data science – bringing models to production and keeping them up-to-date as the data changes. This is probably the most significant technical hurdle to enterprise-wide adoption of data science systems.
Practically, advances such as Open AI‘s famous GPT-3 system are allowing for better conversational interfaces, improved auto-completion, and even automated code completion for software developers.
AI is a powerful tool and can be used for good or for ill. As adoption and the range increases, organizations and even regulators want to ensure safe, explainable, and equitable use. Technologies and processes that enable this are an active research area and have started coming to market. In the next few years, we will probably see more adoption around industry and regulatory standards.
As the ability to model language and images rapidly improve, we are seeing AI systems begin to grow more refined as well. Practically, advances such as Open AI‘s famous GPT-3 system are allowing for better conversational interfaces, improved auto-completion, and even automated code completion for software developers.
What are some things every business should know about data science?
There’s a lot business leaders should know about data science, but here are some misconceptions I regularly hear that I often address with people.
Firstly, data science isn’t a magic bullet – unfortunately, you can’t just throw data into a machine learning algorithm and expect it to spit out insights. We actually follow a process – in our case, the classic scientific method – to prepare data, select algorithms, and generate mathematical outputs that we can then turn into valuable insights. This process takes time, expertise and nuance, but it is worth the effort.
Secondly, you don’t need a graduate degree to be an excellent applied data scientist. I see many companies wanting to hire a Masters’ graduate, preferably a PhD, with a math or stats background. If you want to solve research problems, then of course, that’s a requirement. But, and I say this as a PhD myself, there isn’t much correlation between this and being a successful data scientist solving business problems. Key skills such as basic calculus, programming, and statistics can be obtained through Bachelor’s degrees, self-study or one of the many excellent data science bootcamps, as well as professional experience successfully executing data science projects.
The goal of Tallan’s Data Science & AI practice is to optimize your entire business by fusing seasoned programming with cutting-edge tools. In addition to bolstering your team when necessary, Tallan’s professionals may develop and deploy models to assist with predictive maintenance and analytics. Learn more or get started with Tallan’s Data Science Modernization solution.