“Alexa, start my evening.” At the command of your voice, the living room lights dim, the music starts, and you’re ready to relax.
Welcome to the world of IoT, or the Internet of Things. However, Alexa and other digital assistants only offer a tiny peek inside that world. IoT is not limited to simple home convenience and automation tasks.
The Internet of Things bridges the material and digital world. Powered by wirelessly networked hardware devices and sensors, IoT technology transmits data, monitors information, and initiates actions in physical systems.
IoT’s power, versatility, and possibility range from the lightbulbs, coffeemakers, and thermostats in our homes to the cities, cars, airplanes, factories, and energy grids that build, move, and power our economy.
The Internet of Things is the physical manifestation of the information economy. It is on the front line of a new industrial revolution.
Industry 4.0: Big Data and the Internet of Things
We typically speak of the industrial revolution as if there has only been one. Industrial technology has continually evolved since coal-fired boilers powered the first steam engines. Sometimes that evolution is a revolution, transforming societies and economies, from steam to electricity, computers and integrated circuits to big data, sensors, and networks.
From Steam to Data: Driving Value
Today, we are well into our third revolution and stepping headlong into the fourth: Industry 4.0. Powering this new industrial revolution is data and what we can now do with it in the real world, in real-time.
“Artificial intelligence, machine learning, big data, and data science enable the benefits promised by IoT,” says Chris Healey, an adjunct professor in data science and analytics at Merrimack College.
The intersection of these technologies, coupled with an expanding web of IoT sensors and edge computing, portend a “technological revolution that will fundamentally alter the way we live, work, and relate to one another,” writes Klaus Schwab in a World Economic Forum article. “The possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited.”
We must temper Schwab’s breathless optimism with an understanding of the challenges to such possibility and promise. Acquiring data from a widening circle of inputs does not effortlessly translate to benefit. For that, we need competent, well-trained data scientists to lead the way. Professionals who are skilled in the technology, aware of its ethical implications, and adept at aiding collaborative decision-making among stakeholders.
“Because of the vast amount of IoT data available, there is no way that standard data warehousing, engineering, and processing best practices can be utilized,” Healey says. “We have to use data science and machine learning to translate these data streams into value.”
Benefits and Applications of IoT
IoT changes how we use and interact with everyday objects. “IoT devices provide the link between devices, humans, and the cloud,” says Healey.
“While there are interesting applications of IoT everywhere, I think most of the best applications exist where computing is already closely coupled with the application. Many opportunities with IoT lie in wearable health, autonomous vehicles, the smart grid, and industrial automation.” Let’s take a quick look at each.
We’ll examine the potential of IoT and why the “staggering amount of data” in IoT applications “require new technologies to transmit, store, and process data.”
In 2021, the global wearable health devices market reached a valuation of nearly $19 billion. Projections suggest a compound annual growth rate (CAGR) of 22.2% starting in 2022, reaching a market cap of $60.55 billion by 2027.
But wearable health extends to critical real-time monitoring for chronic illnesses. Devices either worn, implanted in the body, or attached to clothing monitor heart disease, diabetes, and neurological disorders.
“Wearables are revolutionizing the doctor-patient relationship by providing information about how you feel that enables data-driven, actionable treatment,” writes Sandeep Ozarde on LinkedIn. “Wearables can also enable patients to better engage in self-care and the healthcare ecosystem.”
The Smart Grid
There is arguably no other IoT application more consequential to our long-term prosperity than the smart grid.
“The Smart Grid is a unique chance to transform the energy sector into a new age of dependability, availability, and efficiency, which will contribute to our economic and environmental health.” states an article in Security Boulevard.
The smart grid, renewable energy sources, and energy storage capacity are fundamental to this new energy economy. The Internet of Things controls, monitors, and maintains the smart grid. IoT devices respond to external environmental inputs, balancing loads, and increasing efficiency.
In short, IoT is critical for scaling up a smart, clean, and resilient energy grid.
Like everything else listed here, the Internet of Things is an essential ingredient for the future of industrial automation. When harnessed with AI and machine learning, IoT “builds on progress made during the third industrial revolution that sparked the adoption of computers and automation,” says Guy Yehiav in Forbes.
From the factory floor to the global supply chain, embedding connected sensors across a production chain is like digital grease on the wheel of industry.
Smart industrial automation “increases reliability, provides predictive maintenance analysis, reduces operating costs, detects human errors, and determines root-cause errors,” Merrimack College’s Chris Healey explains.
Self-driving cars are the poster child of industrial automation and IoT applications. The automotive IoT market was valued at $8.7 billion in 2021. Projections forecast a CAGR of 25.5% with revenue of $286.8 by the end of 2028.
The benefits and potential of IoT apply to commercial and private ground transportation applications. Automotive IoT transforms how we move on our roads and highways– from self-driving applications to safety, navigation, predictive maintenance, vehicle security, and fleet management.
Help Lead Industry 4.0: Earn a Master’s in Data Science
The competency-based curriculum provides students with the technical expertise and business insight to influence decision-making and turn “data into value” in a rapidly changing industrial landscape.
An industry advisory council guides the top-rated program to ensure students learn the practical skills required in today’s economy. Graduates emerge ready to employ their knowledge and expertise to lead, adapt, and apply cutting-edge IoT, AI, machine learning, and predictive modeling solutions to real-world problems across all sectors of society. Expert faculty with industry experience teach the 8-course, 32-credit hour program.
Six learning outcomes encompass the curriculum, each focusing on a fundamental data science skillset. Students complete 5 core courses and 3 electives.
These learning outcomes and courses include:
Learning Outcome 1: Formulating Problems
- Core: Introduction to Data Science and Statistics
- Students become familiar with the data science field. They explore its applications and use cases. Students also learn relevant statistical topics with applications in data science
- Data Governance and Privacy
- Examine data capture-related rights and responsibilities. Topics include data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing. operational and experiential aspects of data governance and differential privacy.
Learning Outcome 2: Collecting and Processing Data
- Core: Python & R Programming
- Learn to write and read Python and R. Using these languages, students program codes for exploring, summarizing, and visualizing numerical, categorical, date, and text data.
- Big Data: SQL/NoSQL Programming
- Students learn to write and read SQL and no-SQL queries. The course provides an understanding of the design and function of relational and non-relational databases.
- Hadoop and Spark Data Processing
- Gain a comprehensive understanding of the big data processing foundation and techniques. Students learn the basic concepts of parallel computing, big data, Hadoop, MapReduce, and Spark. With this understanding, they develop the skills to solve big data processing problems.
Learning Outcome 3: Analyzing and Modeling Data
- Core: Artificial Intelligence and Machine Learning
- Explore approaches to automated pattern detection focused on unsupervised and supervised learning, feature engineering, classification, regression, and neural networks.
- Predictive Modeling
- Fitting and validation of multivariate predictive models concentrating on the estimation of continuous or categorical outcomes.
- Unstructured Data and Natural Language Processing
- Examine sentiment analysis with logistic regression and naïve Bayes; dynamic programming, hidden Markov models; encoding, decoding, and machine translation.
Learning Outcome 4: Presenting and Integrating Results into Action
- Core: Visual Data Exploration with Tableau and R Shiny
- Learn to use industry-leading software to “tell the story of the data” by creating graphical summaries using Tableau and interactive dashboards using R Shiny.
- GIS Information Visualization
Tell stories with data linked to maps, 2D and 3D spatial analysis using industry-leading software ArcGIS. Coordinate systems, projections, layering, raster, and vector data
Learning Outcome 5: Real-World Applications of Data Science
Students can choose one of three options:
- Social Media Analytics
- Use analytical techniques to convert social media data into marketing insights, benefits and limitations of social media listening, creation of monitors, discussion of standard social media metrics, market structure, and consumers’ brand perception.
- Sports Analytics
- Apply analytical techniques including multiple regression and discriminant analysis to predict player performance and team outcomes as well as business models for sports franchises.
- Healthcare Analytics
- Hands-on analytics of real-world healthcare datasets. Students apply their modeling skills to real-life applications in the health care environment.
Learning Outcome 6: Capstone
Students choose one of three options for their core capstone project:
- The instructor provides data and project requirements.
- Students provide their own data.
- Students complete an industry internship.
An Inflection Point
We stand at an inflection point in a complex, post-pandemic, climate-stressed world. It is a world driven by data, with the divide between the digital and physical realms ever more blurred.
The Merrimack College Master of Science in Data Science degree program is the path for motivated data science professionals to reach their potential as they guide business and society into Industry 4.0.