Data Science.

What Is Data Science? A Complete Guide.

Data Science 101

Put simply, data science refers to the practice of getting actionable insights from raw data. Our guide will walk you through the ins and outs of the data science field, including how it works and examples of how it’s being used today.

Data Science Definition
What Does a Data Scientist Do?
Data Science Applications
Data Science Definition
big data
Data science refers to the process of extracting clean information to formulate actionable insights. | Image: Shutterstock

What Is Data Science?

Put simply, data science is devoted to the extraction of clean information from raw data to form actionable insights.

And there are lots of data out there. By 2025, it’s estimated there will be around 175 zettabytes of data floating around (a zettabyte is a trillion gigabytes). Data has been called the “oil of the 21st century.” So, what do we do with all of this data? How do we make it useful to us? What are its real-world applications? These questions are the domain of data science.

What Is Data Science?

Data science is the process of using tools and techniques to draw actionable information out of huge volumes of noisy data. Data science is used for everything from business decision making to sports analytics to insurance risk assessment.

The data science field is growing rapidly and revolutionizing so many industries. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent search engine query 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 data lakes, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.


Data Science Lifecycle

Data science involves several disciplines to produce a holistic, thorough and refined look into raw data. While some data scientists specialize in narrow areas of the field, others are generalists and have skills spanning everything from data engineering, math, statistics, advanced computing and visualizations, and are 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 often 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.

Data science can be thought of as having a five-stage life cycle:

  • Capture — This stage is when data scientists gather raw and unstructured data. The capture stage typically includes data acquisition, data entry, signal reception and data extraction.
  • Maintain — This stage is when data is put into a form that can be utilized. The maintenance stage includes data warehousing, data cleansing, data staging, data processing and data architecture.
  • Process — This stage is when data is examined for patterns and biases to see how it will work as a predictive analysis tool. The process stage includes data mining, clustering and classification, data modeling and data summarization.
  • Analyze — This stage is when multiple types of analyses are performed on the data. The analysis stage involves data reporting, data visualization, business intelligence and decision making.
  • Communicate — This stage is when data scientists and analysts showcase the data through reports, charts and graphs. The communication stage typically includes exploratory and confirmatory analysis, predictive analysis, regression, text mining and qualitative analysis.

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What Does a Data Scientist Do?

What Is a Data Scientist?

Someone who specializes in the process of collecting, organizing and analyzing data so that the information therein can be conveyed as a clear story with actionable takeaways. As a general rule, data scientists are skilled in detecting patterns hidden within large volumes of data, and they often use advanced algorithms and implement machine learning models to help businesses and organizations make accurate assessments and predictions. The typical data scientist has deep knowledge of math and statistics, as well as experience using programming languages such as R, Python and SQL.


Data Scientist Careers 

Data science jobs can come in many different forms. During the beginning of a career in data science, a person may hold the title of data scientist and progress to analyst, engineer, architect and so on. Each role within data science uses both technical and soft skills that will need to be developed throughout a person’s career. 

Data Science Roles

  • Data Scientist handles data collection, analysis and visualization; sometimes builds machine learning models.
  • Data Analyst is responsible for collecting, cleaning, analyzing and reporting data; sometimes tracks web analytics.
  • Business Analyst uses data to make actionable business insights for the rest of the organization.
  • Data Engineer designs, builds and maintains data pipelines; test ecosystems for data scientists to run algorithms.
  • Machine Learning Engineer designs and builds machine learning systems.


Data Science Skills

There’s no one-size-fits-all answer to the question what does a data scientist do? So the exact skills and toolboxes that data science professionals need vary from role to role.

That said, there are some general proficiencies to acquire that will set up aspiring and early-career data science professionals for success. Those include skills in:

  • Programming — using languages like Python and R.
  • Database management — learning and applying SQL to communicate with databases.
  • Statistics — having a handle on how to analyze data to solve problems.

Additionally, successful data scientists often possess a few key soft skills such as:

  • Curiosity — focused on figuring problems out and always learning new things.
  • Storytelling — the ability to tell stories with data and relay insights.
  • Communication — comfortable collaborating with others and communicating problems and solutions clearly.

Of course, there are other skills and techniques that data scientists will need to learn if they wish to enter into more specialized fields within data science, such as deep learning, neural networks and natural language processing.

Data Science Applications

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:

Data Science Examples and Applications

  • Anomaly detection (fraud, disease and crime)
  • Classification (background checks; an email server classifying emails as “important”)
  • Forecasting (sales, revenue and customer retention)
  • Pattern detection (weather patterns, financial market patterns)
  • Recognition (facial, voice and text)
  • Recommendation (based on learned preferences, recommendation engines can refer you to movies, restaurants and books)
  • Regression (predicting food delivery times, predicting home prices based on amenities)
  • Optimization (scheduling ride-share pickups and package deliveries)

Here are a few more, in-depth examples of how businesses use data science to innovate and disrupt their sectors, create new products and make the world around them even more efficient:


Data Science in Healthcare

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. The sensitivity of patient data makes data security an even bigger point of emphasis in the healthcare space.


Data Science in Self-Driving Cars

Data science is showing up on the road too. Tesla, Ford and Volkswagen have implemented predictive analytics in their 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.


Data Science and Logistics

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 and construction. It’s estimated that data science is saving the logistics company millions of gallons of fuel and delivery miles each year.


Data Science in Entertainment

Do you ever wonder how Spotify 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, these media streaming giants learn your preferences to carefully curate content from their vast libraries they think would accurately appeal to your interests.


Data Science in Product, Sales and Marketing

Many businesses rely on data scientists to build time series forecasting models that help with inventory management and supply chain optimization. Data scientists are also sometimes tasked with making proactive recommendations based on budget forecasts made through financial models. Some even use data mining to segment customers by behavior, tailoring future marketing messages to appeal to certain groups based on previous brand interactions.


Data Science in Finance

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 platform uses natural language processing to process and extract vital data from thousands of commercial credit agreements a year. Thanks to data science, what would take around hundreds of thousands manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal invest in data science to create machine learning tools that quickly detect and prevent fraudulent activities.


Data Science in Cybersecurity

Data science is useful in every industry, but it may be the most important in cybersecurity. For example, international cybersecurity firm Kaspersky uses science and machine learning to detect hundreds of thousands of 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.

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