What do data science, big data, and data engineering have in common? If you’re like most people, you’ve probably never heard of these terms before (and definitely not together). But that could soon change—these concepts are becoming increasingly relevant in today’s society, and they will likely continue to gain traction in the future. So what exactly are data science, big data, and data engineering? How are they related? And how are they going to change the world?
From data engineering to data science
Data science is a new, interdisciplinary field that integrates statistics, data analytics, machine learning and high-performance computing to develop intelligence from massive datasets. The emergence of big data has inspired organizations to use advanced analytics to gain deeper insights into their customers and operations than ever before. To unlock these new opportunities for growth and innovation, companies are increasingly adopting an integrated data engineering/data science approach that enables them to act on large volumes of structured and unstructured data. Data engineers collect and store data in databases, detect patterns or anomalies in streams of data, transform or normalize data for analysis and make it available for consumption by analytics applications.
Data scientists turn all of these technologies into business value by extracting knowledge from information using sophisticated algorithms. In short, data science can be viewed as data engineering with brains—and vice versa. They complement each other nicely: Data engineers handle storage and movement of data, while data scientists can do much more with all that data once they get it into a form they want. Because they work together so closely, these two positions should be filled by individuals who not only understand both sides but also have complementary skillsets and complimentary personalities.
This symbiotic relationship between data science and data engineering will help ensure optimal outcomes for today’s businesses—whether we’re talking about fraud detection at a financial institution or personalized healthcare at a health insurance company.
As such, it’s essential to look beyond traditional job titles when hiring for these teams; after all, what does it matter if you hire people to fill roles labeled data engineer or data scientist if those people lack appropriate technical skill sets? You must instead focus on finding candidates who possess specific technical experience as well as soft skills necessary to succeed in a collaborative environment focused on delivering results. It takes time and effort to build strong teams capable of meeting these requirements, but doing so will enable your organization’s data strategy to thrive now and far into the future.
How machine learning works
Machine learning is a method of artificial intelligence that enables computers to learn without being explicitly programmed. In particular, machine learning focuses on a set of algorithms that can take data as an input and output an appropriate response. Using machine learning, you can teach a computer to perform an automated task—for example, playing chess—without having to code every possible chess game.
A simple example of machine learning is spam filtering. You can use a machine learning algorithm to determine which emails are likely to be spam and then train it on hundreds or thousands of examples of spam and non-spam emails.
After training, you can ask your system whether a new email is spam; if it isn’t, you know that your system made an error, so you either flag it as such or feed it back into your training dataset to further improve performance. In essence, machine learning offers computers common sense—something humans take for granted but that computers still lack.
Machine learning is used in many places today. It’s at work in Google Translate, Amazon product recommendations, Facebook friend suggestions, Netflix movie recommendations, and more. Machine learning is also playing a huge role in medicine: it can help doctors identify diseases based on symptoms, predict how likely a patient is to respond to a treatment based on their genome, and even use data from electronic medical records to determine which patients are at greatest risk of readmission after being discharged from a hospital.
Machine learning uses artificial intelligence, what is it?
Machine learning is a field of computer science that uses statistical techniques to give computers the ability to learn (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. In essence, machine learning automates analytical model building for predictive analytics—automating repetitive tasks that humans would otherwise need to do. Machine learning essentially automates complex analysis by using two types of approaches: supervised and unsupervised.
Supervised learning is when computer programs receive a set of input data and, based on those inputs, can learn to predict an output value. For example, your company might use machine learning in a recruitment process to identify job candidates with a high probability of success. Candidates are given a series of tests based on their resumes and interviews, and that data is used to train machine learning algorithms to predict who will succeed in the role.
Unsupervised learning does not require that the computer be given an output value. The goal is to enable computers to find patterns in data and make predictions based on those patterns. For example, your company might use unsupervised machine learning algorithms to predict fraud in a payment system by monitoring transactions for unusual activity. With no criteria provided, computers analyze all available data and look for any abnormal trends, helping your business more effectively monitor financial activity.
Neural networks, a form of machine learning algorithm, are organized in layers that each have a specific task. The output of one layer becomes an input for another layer. This process continues through all layers until all data has been used and predictions made. Deep learning is a type of neural network where many layers are used. The accuracy of deep learning algorithms is particularly high in image recognition, but it can also be applied to other processes such as speech recognition and language translation.
Supervised, unsupervised, and deep learning are all useful for different applications, but it’s important to understand how they work. Machine learning is a powerful tool that can help businesses make predictions from data sets and continuously improve performance on a range of different tasks. Whether you’re using machine learning to automate predictive analytics or use neural networks to reduce fraud in your business, it has some very clear benefits—as well as some associated risks.
The benefits of machine learning can be useful across an array of business applications, but there are a few different use cases that have a particularly high value. In retail, for example, machine learning can be used to predict customer behavior and improve product recommendations. Machine learning algorithms take customer data from previous purchases and analyze it to create new product features. These features might include customer preferences or insights into what products customers will buy in combination with other products. This helps businesses make better predictions about future purchases and sell more effectively.
Machine learning is also useful in customer service applications. By analyzing social media and online feedback, machine learning algorithms can help businesses better understand how to improve customer experience. A range of different data points including payment history, delivery preferences, and social media engagement can all be used to optimize customer experience and create a better interaction with your brand. This helps you attract new customers without spending significantly more on marketing campaigns or other branding initiatives.
Why it matters?
Data science may be a hot field these days, but data has always mattered. It’s how we measure our world and understand it. It’s why we can locate ourselves on a map, take selfies and send emails to friends. Data science is simply taking one of mankind’s oldest tools—data—and using it in more innovative ways than ever before. But with those innovations come exciting new opportunities for professionals to use their analytical skills to solve problems and ultimately change lives. Here are three areas where data scientists will have an outsized impact:
1) Precision medicine
2) Predictive analytics
3) Augmented (or virtual) reality
The health care industry is a prime example of an industry that’s ripe for transformation through data science. Health care professionals have access to lots of data, but it’s often scattered in different locations and formats, making it difficult to use effectively. But gathering and organizing all that data is critical to providing patients with affordable, high-quality care. The Affordable Care Act was intended in part to increase access to healthcare coverage and make health insurance more affordable for more Americans.
That’s where data science comes in. In a recent survey, 95 percent of doctors said they used electronic health records at their office and 66 percent said they use them at home. When you combine all that data, physicians can get a clearer picture of how diseases progress and how different treatments impact patients over time. This enables doctors to recommend treatment plans customized to each patient’s individual needs.
But data science isn’t just for doctors—it can also be used to help patients take better care of themselves. Data from wearable devices and health apps can be aggregated and analyzed to spot patterns in how people live their lives and learn new things about how our bodies work. This will help people make more informed decisions about their health, leading to better health outcomes overall. Plus, it can encourage patients to become more active participants in their own healthcare decisions, reducing costs across the board.
You might not see augmented or virtual reality devices replacing your smartphone just yet, but their impact is going to be huge. AR and VR technologies give us a new way to interact with each other and our world. That will fundamentally change how we live, work and play—and it means big opportunities for people who know how to create cutting-edge software.
Businesses are always looking for innovative ways to streamline operations and keep costs low. With so much software out there, it can be tough to know which is actually worth using—and what’s just a waste of money. That’s where data science comes in. The right analytics solutions give you all-important insights into how your operations are performing, allowing you to get more from your existing software investments or select new software that will help you grow and scale with minimal risk.