Data science, the Internet of Things (IoT), and predictive analytics are all hot topics in business and technology right now, but what are the differences between these three popular fields? Are they synonymous? Are they related? And, most importantly, which should you focus on if you're looking to get into one of these fields? Let's break down the similarities and differences between IoT, data science, and predictive analytics so that you can decide which one fits your interests best.
The Internet of Things (IoT) refers to all devices connected via the internet or by a common means (like Bluetooth, RFID, or Wi-Fi) that use automatic data collection, automated machine learning, and sharing information with other IoT devices without human intervention. For example, you can see your home's temperature displayed on your smartphone while walking into a store; when you approach your front door, you are notified to get an umbrella because it is about to rain in half an hour from now; your digital calendar will automatically know that today is father’s day so it recommends you a book about fatherhood for him...A mass surveillance society like Orwell predicted! On one hand, we have a lot of conveniences brought by smart devices and sensors. On another hand, we have more privacy issues than ever before. This is where Data Science comes in handy! By studying how people interact with different types of sensors, what they search online, and what they purchase offline, companies can understand their customers better than ever before. However, there is still not enough talent available who can work on real-world problems using both Data Science & Internet Of Things technologies together. Therefore, companies must look at investing more resources into training their employees as well as hiring professionals who possess both skill sets but also have domain expertise related to their business needs.
Data Science vs Internet of Things
What’s the difference between data science and IoT (Internet of Things)? These two terms seem to be thrown around quite a bit lately. Both involve collecting data and analyzing it, but there are key differences that will affect how you should use them in your business or personal life. To answer what is Data Science and what is the Internet of Things, let’s take a look at each technology individually first before diving into some examples. Then we can decide which one best fits your needs. Let’s start with the Internet of Things!
The term Internet of Things was coined by Kevin Ashton, co-founder and Executive Director of Auto-ID Labs at MIT. It describes all devices connected via an internet connection. This could include refrigerators, microwaves, cars, phones, computers—anything with an internet connection! These devices collect information about themselves and their environment using sensors that gather real-time data. They then transmit those bits of information over a network where they are stored in a database somewhere. That database might live on an external server or even on a hard drive inside your computer!
Difference Between Data Science and Internet of Things in Tabular Form
|Basis For Comparison||Data Science||Internet of Things|
|Define||Data science is an interdisciplinary field that has emerged from computer science, statistics, and applied mathematics; it is concerned with extracting knowledge and insights from data in various forms, including structured and unstructured data||The Internet of things is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these objects to connect and exchange data|
|Time Period||It has human analysis and is, therefore, time-consuming.||It has smart devices that process and transmit data quickly or in real-time.|
|Use||It uses smart information from big sets||Mechanical running|
|Deal with||Human and machine-generated data.||Machine-generated data.|
What is Data Science?
Data science is an interdisciplinary field that has emerged from computer science, statistics, and applied mathematics; it is concerned with extracting knowledge and insights from data in various forms, including structured and unstructured (e.g., text-based) data. Data scientists are expected to have skills at making sense of massive amounts of disparate information gathered from multiple sources using techniques in statistics, machine learning, artificial intelligence, and high-performance computing among others to generate actionable insights for business management or marketing purposes. Some key sub-disciplines within data science include predictive analytics, which attempts to identify patterns that suggest future outcomes based on past behavior; prescriptive analytics, which aims to optimize processes based on forecasts of future behaviors; computational social science, which seeks to understand human behavior through statistical analysis of large sets of historical records such as social media postings or e-commerce transactions; business intelligence (BI), which combines analytic tools with databases and reporting dashboards so organizations can make better decisions by improving access to their data assets within a context relevant for decision-makers.
Advantages of Data Science
The internet and big data have changed our lives over these last few years and there is no turning back now as data scientists are in demand everywhere! They are getting involved in almost all industries, solving various problems that were too expensive or difficult to do earlier with traditional methods. To define it, Data science is a field that helps businesses collect, store, analyze and report information they can use to make better decisions based on real-time actions that they track on websites, via apps, etc. In short, data science is processing huge amounts of information derived from customer interaction to gain customer insights which ultimately help improve business results. The future holds a lot for data scientists who have so much power in their hands today thanks to technology! They get paid well to solve many business problems through analytical means like applying algorithms and statistical models, clustering customers into groups based on behavior patterns, or building predictive models that can forecast customer behavior. There are some other specializations within data science such as machine learning, artificial intelligence, etc., but they don’t hold more importance than pure data science
Disadvantages of Data Science
Data science has to choose its tools and technologies, which is a lot easier said than done in practice. To continue with a simple example, consider you want to use machine learning to classify email messages by topic. Well, you might decide that because your data is unstructured (just free-form text), it makes sense to use deep learning or some other sort of artificial intelligence technique, since those tend to work best on semi-structured data like texts. But if you do that, then which framework should you use? Probably not TensorFlow if you want your software stack and codebase maintainable over time! Even worse, once you’ve chosen one technology for one problem, there’s no guarantee that the same technology will be appropriate for another problem down the road. This isn’t just a theoretical concern—it happens all too often in practice. If it seems like I’m being overly negative about data science here, well...I am! The reason is that my experience as an entrepreneur leads me to believe that startups don't have enough resources to get everything right 100% of the time. So, they need every advantage they can get—and I think having good data scientists on staff provides exactly such an advantage.
What is the Internet of Things?
IoT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these objects to connect and exchange data (such as measurements and commands) automatically with no human-to-human intervention required beyond setting things up in the first place. The IoT allows objects to be sensed or controlled remotely across existing network infrastructure using advanced technologies like wireless communication, GPS navigation, SaaS (cloud computing), Big Data analytics, and so on. This makes it possible to track or control remote objects without being anywhere near them. An example would be an inventory management system that tracks stock at different locations, even when there’s no one at those locations. It can also make it easier to manage tasks such as tracking deliveries and shipments or monitoring for potential problems. And because many IoT applications are web-based, they can also make it easier for people to access information about their everyday lives from any location using a smartphone or computer. For example, you could use your phone to check on your thermostat settings while you’re away from home or view video footage captured by your security cameras while you’re out running errands.
Advantages of the Internet of Things
Some experts predict that by 2020, there will be around 28 billion IoT devices in use worldwide; while others go as far as to say there will be 50 billion by 2030. To handle all that data, companies need systems that can process it as quickly and efficiently as possible. That’s where big data and analytics come in handy — they allow businesses to collect massive amounts of information in a short period, which is then turned into actionable insights that boost productivity and business processes across the board. To learn more about how you can benefit from these technologies, check out our guide on Big Data vs. Analytics here.
Disadvantages of the Internet of Things
IoT devices are a new technology that has attracted both excitement and worry from different quarters. The costs of sensors have declined, which is why devices like Nest thermostats are feasible for consumers today. These sensors collect data about your activity at home and send it to a cloud service to analyze and make predictions about what you will do in your daily life, therefore creating a virtual representation of you as an individual over time. However, some concerns have arisen from analyzing IoT devices due to their invasiveness or security problems they create because they often connect directly to your computer via a USB port or Wi-Fi connection without any protection mechanisms such as VPN tunneling would enable secure communications between two endpoints. For example, if a hacker can access your webcam through malware installed on your computer, he/she could potentially watch you remotely and record video footage of you without even knowing. This is just one example; there are many more possible scenarios where IoT devices could be used against us in ways we haven't even thought of yet. If we want to avoid these kinds of situations, we need to take better care when implementing these technologies into our lives so that everyone can benefit from them equally.
Main Difference Between Data Science and Internet of Things in Points
- Data science is a broad term covering many aspects related to data and applying machine learning, statistical analysis, etc.
- It’s quite different from IoT which doesn’t include these machine learning or statistics aspects and it’s more focused on connecting devices on the internet through software platforms like Thing Worx and cloud environments with the Internet of things operating systems.
- Although more points and differences between these two can be talked about as a short answer, we consider these differences: - The Internet of things is a connected devices ecosystem (networked) to send data to the Cloud for analysis using a dedicated OS whereas Data Science analyses all sorts of data (like text, video, sound) and user interaction at client side without involving sending data to cloud infrastructure thus cost-saving network bandwidth, etc.
- Also, the Internet of things involves only one type of device while data science involves various types of devices and platforms. - Data Science has a wider scope than IoT because it covers all sorts of data while IoT just covers connected devices via cloud or local gateway.
- The most important point is that you cannot compare these two things because they have completely different purposes, business models, technology stacks, and so on.
As both fields continue to grow and expand, their relationship with one another will grow as well. Companies that combine their data science and IoT platforms will be best poised to profit from what each field has to offer as they evolve in different directions in preparation for an even brighter future together. The key is having a strategy in place to take advantage of how each field complements and benefits from working with one another. By creating more ways for your company’s two-way street between these two disciplines, you can make sure your business is ready when it comes time to capitalize on what comes next.
The common factors between data science and IoT are going to become much more important over time. For example, if we see things like smart cities come into existence—with sensors collecting information about traffic patterns or air quality—those companies that can bring their analytical skillsets into play around big data will have a huge leg up on those who aren’t able to keep up. At some point shortly, it may be very difficult for any business to succeed without an understanding of how each field works together. By combining these two disciplines into one overall strategy for your company, you can make sure you don’t fall behind as these two fields continue to evolve in different directions.