In 1965, Gordon Moore predicted that the number of transistors that could fit on a microchip would double every two years. The accuracy and longevity of what came to be called “Moore’s Law” surprised even Moore. There is an analogous path for the future of data science.
Many articles expound on the “death of Moore’s Law.” However, the rise and future of data science, Big Data, Artificial Intelligence (AI) , and machine learning breathe a second life into Moore’s principle.
As peculiar as it may sound to us now, the computers of the 1960s were marvels of their time. Even so, Moore’s vision for the technology — and a way of life — was set to explode. Data science and its attendant methodologies are no different. As ubiquitous as machine learning algorithms and advanced data science has become, we are still at the base of an upward trend.
“In 10 years, data scientists will have entirely different sets of skills and tools,” writes Eric Miller in Infoworld. Let’s briefly explore the current landscape of data science and what to expect in the coming decade.
The Future of Data Science
Data scientists are not immune to role-changing technology. Indeed, it is their expertise that drives a shifting landscape in the profession.
The Bureau of Labor Statistics forecasts a 22% increase in data science jobs through 2030. With rising demand comes rising salaries. The median annual salary in 2020 for “Computer and Information Research Scientists” was $126,830.
Our data-driven economy will always require data scientists, but their roles will evolve, as have other critical professionals throughout history. The job will become “more strategic and collaborative,” says Miller.
Following are the trends and technologies that will inform the future of data science.
Automated Machine Learning
Automated Machine Learning, or AutoML, is a big step in “democratizing” data science. Big Data is the feedstock of machine learning algorithms from which analysts can extract meaning. However, building machine learning models typically require data scientists and computing resources up to the task. Most organizations don’t have the resources required to implement traditional machine learning. AutoML is an end-to-end solution to this constraint, automating the process from a raw dataset to a functional machine learning model.
Benefits of AutoML include:
- Increased productivity: Automating repetitive processes increases productivity, freeing data scientists from rudimentary tasks to focus on the end goal of problem-solving with the model.
- Reduce the potential for errors: Automation helps reduce mistakes in misconfiguring parameters and misinterpreting data.
- Widespread access to machine learning: AutoML levels the playing field and provides access to the power of machine learning to non-experts.
The Internet of Things (IoT)
IoT is already in our homes and businesses but stands on the cusp of revolutionizing everything from industrial operations to city planning. Integrating IoT with AI leverages Big Data which can then automate multiple business processes.
In manufacturing, embedded sensors enable remote monitoring at every production line stage and facilitate predictive maintenance. With predictive analysis, IoT systems increase worker safety and efficiency.
Data-driven supply chain management is possible with IoT devices using GPS and RFID tags. As automation and RFID technology progress, some claim that humans may become obsolete in the supply chain (presumably not including the producer and end-user). In the coming years, manufacturing will increasingly involve IoT devices connected to real-time, cloud-based data analysis.
Investment in IoT technology is expected to top $1 trillion by 2023.
The growth of IoT, AI, and big data in every economic sector precipitate the rise of edge computing.
Essentially, edge computing brings data storage and processing closer to the devices using those resources–in other words, closer to the edge.
Edge computing is an alternative to the typical high-end storage and large bandwidth requirements of big data analytics. Where cloud computing has numerous advantages over on-site computing, edge computing takes it another step further.
The expanding infosphere is rapidly rubbing up against the inherent limits of centralized cloud-based systems. Edge computing overcomes these limitations, helping ensure the future promise of AI, big data, and IoT.
A Great Time for Data Scientists
It is an exciting time to be a data scientist. The transformative power of big data analytics, machine learning, AI, and IoT is well-proven, evident in nearly every aspect of the world around us. Yet, that transformation is only beginning to realize its potential. We are in the early stages of Moore’s Law-like growth in the capabilities of data science. At the same time, technologies such as AutoML broaden the playing field of its possibility.
But even if the role of data scientists will be different in 10 years, “their function will remain the same,” says Eric Miller in the Infoworld article, “to serve as confident and competent technology guides that make sense of complex data.”
Professionals who prepare now for the future of data science position themselves as leaders in our data-driven world.
Master of Science in Data Science: Prepare Now for the Future
The online Master of Science in Data Science degree program from Merrimack College gives students the skills they need to thrive in a rapidly evolving discipline. Students also learn the practical communication skills important in translating complex ideas into practical terms that all stakeholders can understand.
The top-rated industry-aligned program consists of an eight-course, 32 credit hour curriculum. Leading industry experts teach each course, bringing a wealth of real-world experience to the classroom. Instruction combines self-paced learning with live, online instructor-led sessions.
- Foundations of Data Management
- Foundations of Statistical Analysis
- Data Visualization
- Data Exploration
- Data Governance, Laws & Ethics
- Predictive Modeling
- Machine Learning
- Data Science Capstone
The capstone allows students to apply what they’ve learned to real-world challenges. From raw data to a functioning model, students tackle specific industry problems.
Graduates emerge from the Master of Science in Data Science program ready to meet today’s challenges in a globalized information economy. What’s more, they have the expertise and insight to guide organizations as future challenges, tools, and industries evolve.