What Is Data Science? A Complete Guide.
What Is Data Science?
Data science is a multidisciplinary field of study that applies techniques and tools to draw meaningful information and actionable insights out of noisy data. Involving subjects like mathematics, statistics, computer science and artificial intelligence, data science is used across a variety of industries for smarter planning and decision making.
Data science is the realm of data scientists, who often rely on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
What Is Data Science Used for?
Data science is used by businesses of all kinds, from Fortune 50 companies to fledgling startups, to look for connections and patterns and deliver breakthrough insights. That explains why data science is a rapidly growing field and revolutionizing many industries. More specifically, data science is used for complex data analysis, predictive modeling, recommendation generation and data visualization.
Analysis of Complex Data
Data science allows for quick and precise analysis. With various software tools and techniques at their disposal, data analysts can easily identify trends and detect patterns within even the largest and most complex datasets. This enables businesses to make better decisions, whether it’s regarding how to best segment customers or conducting a thorough market analysis.
Data science can also be used for predictive modeling. In essence, by finding patterns in data through the use of machine learning, analysts can forecast possible future outcomes with some degree of accuracy. These models are especially useful in industries like insurance, marketing, healthcare and finance, where anticipating the likelihood of certain events happening is central to the success of the business.
Some companies, such as Netflix, Amazon and Spotify, rely on data science and big data to generate recommendations for their users based on their past behavior. It’s thanks to data science that users of these and similar platforms can be served up content that is uniquely tailored to their preferences and interests.
Data science is also used to create data visualizations — think graphs, charts, dashboards — and reporting, which helps non-technical business leaders and busy executives easily understand otherwise complex information about the state of their business.
Data Science Tools
Common Data Science Programming Languages
Popular Data Science Tools
• Apache Spark (data analytics tool)
• Apache Hadoop (big data tool)
• KNIME (data analytics tool)
• Microsoft Excel (data analytics tool)
• Microsoft Power BI (business intelligence data analytics and data visualization tool)
• MongoDB (database tool)
• Qlik (data analytics and data integration tool)
• QlikView (data visualization tool)
• SAS (data analytics tool)
• Scikit Learn (machine learning tool)
• Tableau (data visualization tool)
• TensorFlow (machine learning tool)
Data Science Lifecycle
Data science can be thought of as having a five-stage lifecycle:
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.
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.
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.
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.
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.
What Are Data Science Techniques?
There are lots of data science techniques with which data science professionals must be familiar in order to do their jobs. These are some of the most popular techniques:
A type of supervised learning, regression analysis in data science allows you to predict an outcome based on multiple variables and how those variables affect each other. Linear regression is the most commonly used regression analysis technique.
Classification in data science refers to the process of predicting the category or label of different data points. Like regression, classification is a subcategory of supervised learning. It’s used for applications such as email spam filters and sentiment analysis.
Clustering, or cluster analysis, is a data science technique used in unsupervised learning. During cluster analysis, closely associated objects within a data set are grouped together, and then each group is assigned characteristics. Clustering is done to reveal patterns within data — typically with large, unstructured data sets.
Anomaly detection, sometimes called outlier detection, is a data science technique in which data points with relatively extreme values are identified. Anomaly detection is used in industries like finance and cybersecurity.
Data Science 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 analyst and progress to scientist, 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 scientists specialize 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 analysts are responsible for looking for actionable information within data sets, interpreting that data and then creating reports, dashboards and visualizations to communicate those insights to others within the organization and possibly to customers as well. The typical data analyst works with software tools like Tableau and Microsoft Power BI. Unlike data scientists, data analysts are not commonly expected to use advanced statistical modeling techniques, write algorithms or make predictions.
Data engineers design, create and manage systems that data scientists use to access and analyze data. Typically, the job involves building data models and data pipelines, as well as supervising extract, transform and load (ETL).
Business Intelligence Analyst
Business intelligence analysts specialize in analyzing the data related to a company’s performance, the insights of which are communicated across teams to help drive better business decisions. BI analysts are critical in helping organizations understand goals and risks. They also typically can communicate with both non-technical stakeholders and more-technical data scientists within the company.
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.
While there are some 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 — there are some general proficiencies and a few key soft skills that will set up aspiring and early-career data science professionals for success:
Learning and applying SQL to communicate with databases.
Having a handle on how to analyze data to solve problems.
Focused on figuring problems out and always learning new things.
The ability to tell stories with data and relay insights.
Comfortable collaborating with others and communicating problems and solutions clearly.
Data Science Applications
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.
Benefits and Challenges of Data Science
Like most tech innovations that have become integral parts of business and affect our everyday lives, there are pros and cons to consider when it comes to data science.
Benefits of Data Science
• Helps with making predictions and business decisions
• Assists in data analysis, even for complex datasets
• Enhances cybersecurity protection
• Allows for quick business reporting and visualizations
• Optimizes scheduling and recommending services