In a recent lecture, Dr. Carlo Lipizzi, SSE teaching associate professor and graduate engineering management program lead, discussed how to understand and apply data analytics in solving some of the world’s pressing challenges. The following looks at how data has become so integral in most people’s lives and how engineers can make better decisions based on this everyday data.
How can data analytics play a role in helping to solve problems in our modern world?
First, it is important to understand what data analytics is. It’s the science of analyzing data to convert information to useful knowledge. This knowledge can certainly help us understand our world better. In many ways, the knowledge can help us make better decisions. However, the key is how we use the data. It is very complex. We have to mine immense amounts of data and use that data effectively to solve problems. Here at the School of Systems and Enterprises, our approach is to take a systemic view. We consider all the elements, even the subsystems, of complex systems. Then we use many tools, such as mathematical models and algorithms, to derive meaning and gain understanding.
What kinds of problems can be solved?
Data science is becoming an integral part of daily life, with our use of smartphones and smartwatches, webcams, credit cards, E-ZPass and web-sourced radio. Data analytics can be applied to improving everything from healthcare systems to transportation systems. For example, sensors and controls can minimize traffic fatalities and improve traffic flow. In businesses of all kinds, decision makers use data analytics to better serve customer and client needs, expand into new markets, create innovative products and add new jobs to hire more people. That’s just scratching the surface.
What are the key factors in extracting knowledge from data?
Systems are key to the study of data science, but I emphasize to my students that data analytics is an applied theory, not a hard science. You have to be able to look at an immense amount of information and not be intimidated. To extract knowledge from data, we use many skills. Mathematics. Statistics. Machine learning. Databases and computer science. All these skills must be integrated to be effective in finding solutions to problems.
You have said that data science involves craftsmanship. Can you elaborate?
There is a huge component of craftsmanship involved. We are working with processes, not just an application of algorithms and tools. You need to know and interact with your data to get meaning out of it. That means looking at the data from different points of view, taking a systemic approach to understanding the context. As engineers, we build bridges that integrate the competencies of mathematics and technology. At this crossroads, we can help solve problems that are important in our modern world.
For example, let’s say you have a city filled with autonomous vehicles and you have eliminated the traffic lights because the vehicles are interconnected. Are you done? What if someone comes along driving an historic car, not connected to the autonomous vehicles? Most likely, iIt would crash. Engineers cannot work in a vacuum. We are dealing with real world, real-time complexities in a given environment.
To what extent is data science being used to predict future trends?
The problem is, what is true today may not be true tomorrow. We simply do not have the static luxury of truth. And that is why your data must come from many different points of view. Then you will merge the data into a corpus for scientific analysis.
The future cannot be predicted. However, in science, there is a high level of consistency over time. These days there is a lot of interest in embedded analytics, such as smart watches and devices for your home. Data science today is a stepping stone for an even more informed and complex way of living and doing business, with a continuous integration of sources and media, creating semantic synergies, pushing the boundaries of convenience, value and privacy.
How is all the available data affecting our society?
The digital tools we are using every day are creating data from everything we do at an unprecedented rate. For example, every day 2.5 quintillion bytes of data are created, and 90 percent of the data in the world today was created within the past two years. As a result of the digital transformation process, there is a new kind of economy based on the “datafication” of virtually any aspect of human, social, political and economic activity. Individuals, companies, institutions and machines are connected by information generated digitally. There are many studies that suggest that the average attention span is decreasing significantly. How it is affecting our society is something that is very complex.
What advice can you offer for students who are considering the pursuit of data science as a career?
Demand for data scientists is growing. That is the good news. The global big data and business analysis market was valued at $169 billion (U.S.) in 2018 and is expected to grow to $274 billion by 2022. Actually, there is only good news. If you are interested in data, it’s wonderful. If you are a student who is comfortable with classification, clustering, regression, density estimation and dimensionality – the five main areas – then it may be a good option for you.
Any advice about finding a job in the field?
My job search advice is applicable to many fields. I suggest that students apply data mining concepts to their academic career. Go to LinkedIn and Indeed, and check the skills that are in demand in the area that is a target for you. If you want to go into healthcare, automotive or finance, check for jobs in those industries with a keyword like data science or data analytics, and see what skills are most in demand. Then, shape your study plan in a way that can address those points.
Dr. Carlo Lipizzi is an teaching associate professor and graduate program lead at the School of Systems and Enterprises (SSE). He teaches data mining and big data applications on campus, online and in corporate training webinars.
The systems analytics graduate program at Stevens School of Systems and Enterprises prepares students to meet the demand for professionals with the ability to harness complex data and convert it into meaningful information. Our curriculum provides students with expertise in visualizing, manipulating and extracting important concepts from systems data, and complementing it with traditional systems decision-making.