What is Data Science? A Complete Guide.
The simplest definition of data science is the extraction of actionable insights from raw data. Our guide will walk you through the ins-and-outs of the ever-expanding field, including how it works and examples of how it’s being used today.
What Is Data Science?
A groundbreaking study in 2013 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of humankind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2020 that number will balloon to an astounding 44 zettabytes.
What do we do with all of this data? How do we make it useful to us? What are it's real-world applications? These questions are the domain of data science.
Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it's difficult to fence in its capabilities with a formal definition, but generally data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.
Commonly referred to as the “oil of the 21st century," our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.
How Does Data Science Work?
Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
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Data science generally has a five-stage lifecycle that consists of1:
- Capture: Data acquisition, data entry, signal reception, data extraction
- Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture
- Process: Data mining, clustering/classification, data modeling, data summarization
- Communicate: Data reporting, data visualization, business intelligence, decision making
- Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis
1: Source UC Berkeley
All of the five stages require different techniques, programs and, in some cases, skillsets.
Data Science Uses
Data science helps us achieve some major goals that either were not possible or required a great deal more time and energy just a few years ago, such as:
- Anomaly detection (fraud, disease, crime, etc.)
- Automation and decision-making (background checks, credit worthiness, etc.)
- Classifications (in an email server, this could mean classifying emails as “important” or “junk”)
- Forecasting (sales, revenue and customer retention)
- Pattern detection (weather patterns, financial market patterns, etc.)
- Recognition (facial, voice, text, etc.)
- Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)
Additionally, here are few examples of how businesses are using data science to innovate in their sectors, create new products and make the world around them even more efficient.
Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available via everything from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases faster and explore new treatment options.
Tesla, Ford and Volkswagen are all implementing predictive analytics in their new wave of autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.
UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The company’s On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic, construction, etc. It’s estimated that data science is saving the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles each year.
Do you ever wonder how Spotify just seems to recommend that perfect song you're in the mood for? Or how Netflix knows just what shows you’ll love to binge? Using data science, the music streaming giant can carefully curate lists of songs based off the music genre or band you’re currently into. Really into cooking lately? Netflix’s data aggregator will recognize your need for culinary inspiration and recommend pertinent shows from its vast collection.
Machine learning and data science have saved the financial industry millions of dollars, and unquantifiable amounts of time. For example, JP Morgan’s Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and extract vital data from about 12,000 commercial credit agreements a year. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing heavily in data science to create machine learning tools that quickly detect and prevent fraudulent activities.
Data science is useful in every industry, but it may be the most important in cybersecurity. International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.
Computers are great at working with standardized and structured data like database tables and financial records. They are able to process that data much faster than we humans can.