What is data science?
Companies in almost every industry are generating more data now than ever before, thanks to the internet, industrial machine sensors, digitized transactional data, and countless other sources. This has led to efforts to figure out how to leverage this vast amount of data to create new competitive advantages.
That’s where data science comes into play.
Data science as we know it today emerged as a response to this explosion in big data over the past two decades. Data science is a multidisciplinary approach to unlocking value from raw datasets, especially those created by businesses and other large organizations.
The goal of data science is not just to understand data, but to:
- Optimize the processes and procedures that data is capturing
- Use that data to understand what has happened in the past with clarity and detail
- Predict certain outcomes so that changes can be made proactively
Data science ultimately empowers teams to implement and even automate data-driven decisions while demonstrating clear ROI.
Why is data science so important?
Data is being created and stored at a scale that’s difficult to properly conceptualize, even when discussed in the abstract.
Consider that back in 1999, when people were still accessing the internet through America Online, the world produced about 1.5 exabytes of data. By 2025, though, the IDC predicts the total volume of global data will grow to a staggering 175 zettabytes—or 175,000 exabytes.
It certainly seems like all that data should be able to describe and explain everything we need to know about the world around us. But the truth is that data by itself is meaningless until it can be converted into valuable information.
That’s why data science has become such an important field, especially for businesses that are data rich but information poor (as the adage goes).
Here are some of the business-specific advantages of data science:
- Systems optimization: Improve supply chain management, energy usage, or machine maintenance; maximize or minimize inputs or outputs.
- Mitigating risk and fraud: Evaluate credit risk and identify unusual account activity that might suggest fraud for financial services.
- Personalized customer experiences: Create customized messaging and landing pages based on customer data.
- Market research: Identify what products sell best when and where, aiding development, marketing, and delivered delivery.
- Quality control: Analyze data, images, sound, and video to ensure quality and identify problems early on.
- Supporting management: Optimize operations, facilitate smarter and faster decision making, and support business outcomes.
What does a data scientist do?
All of this work is supported by data scientists or by domain experts who use accessible data science tools like RapidMiner to understand data and build machine learning models, without having a background in data science.
Regardless of who is doing data science work, the most basic function of data scientist is to help organizations solve vexing data problems. This problem-solving process often involves elements of computer science, statistics, and business intelligence (more on this below) alongside tools like machine learning and artificial intelligence.
Where these professionals truly add value, though, is in combining these practices with domain expertise so that data inquiries and models all produce functional applications.
This is accomplished through several core actions:
- Data collection
- Data modeling
- Data analysis
- Data problem solving
Data analysis begins with data collection—a systematic process of gathering data, whether from industrial machine sensors, social media posts, point-of-sale transactions, or any other source.
Data may be structured (meaning formatted and searchable), unstructured (meaning in raw form and unsearchable), or somewhere in between.
A data scientist’s first role is to assess what kind of data is necessary to answer particular questions (or solve data problems) and then to devise a scheme for collecting it.
Once data has been collected, it must then be cleaned up. Even structured data is rarely in the exact form needed for analysis—especially not when you’re working with multiple datasets that need to be consolidated.
The process of cleaning, structuring, and enriching raw data into a desired format is known as data wrangling and should be thought of as an extension of data collection.
Data modeling involves evaluating how and where organizational data is being generated and stored. This process can sometimes be quite theoretical. For instance, entity-relationship diagrams may be used which illustrate how different “entities” within a system (like people, objects, or concepts) relate to one another.
Data modeling techniques and methodologies are necessary in order for data to be managed as a resource, especially one that is leveraged for business intelligence. Some level of data collection (and even analysis) is necessary before this step can really begin, but the working model created by data scientists can also serve as a framework to guide and eventually systematize subsequent collection and processing.
Data analysis is the evaluation of data to reveal important insights. Analysis can capture both the number crunching process itself as well as the way information is conveyed to others—whether that’s creating familiar reports for business users or visualizing certain relationships. Data analysis is highly contextual, as the data scientist has to understand what datasets and key variables mean to the organization and its bottom line.
Data problem solving
Businesses don’t really hire data scientists to crunch numbers or construct algorithms—they hire them to generate value. And the first step in value creation is framing the task at hand correctly.
This often means translating ambiguous requests (like predicting which type of loans are going to default in the future) into something more concrete and well-defined (like accurately classifying discrete loans that are at risk).
Data scientists are able to frame problems more effectively when they’ve met with key stakeholders and been brought up to speed on business objectives and other relevant details that will shape how data problems should be contextualized and prioritized.
The varying skills of a data scientist
As noted, data scientists come from different academic disciplines and business backgrounds and play considerably different roles in different organizations. Many of the techniques of data science can actually be taught to existing employees, especially those with some knowledge of analytics.
In general, though, professionals in this field will possess at least some of these core skills:
Statistics is the science of collecting, analyzing, presenting, and interpreting data—so it’s obviously fundamental to data science. No matter their particular research focus, a data scientist will be conversant in core statistical concepts, as well as possess a basic understanding of multivariable calculus and linear algebra.
Computer science presents a toolkit for interfacing with computers (and thus data) and translating theory into application. Data scientists will often have at least some proficiency with programming languages like Python and R as well as database querying languages like SQL. And because machine learning has come to play such an important part in model building, data scientists are increasingly developing skills like natural language processing, logistic regression, and adversarial learning.
Domain expertise simply means that you understand the larger industry or field of study in which you are applying data science. For instance, if you’re performing data science for a manufacturing company, you need to have a sophisticated understanding of supply chains, Six Sigma (and similar approaches to efficiency), economic considerations, emerging technologies, and other key concepts.
Data scientists need to pass along data insights to key organizational stakeholders using visualizations, reports, and presentations in such a way that they prompt action and empower decision-making. That’s why communication competency—meaning everything from persuasion to storytelling to design—is such an essential part of data science, whether those traits show up in the job description or not.
Creating the perfect data science toolbox
Data is everywhere in large operations and many professionals are involved in managing it. That means data science is ultimately a team effort. Everyone plays a role, and everyone should be aware of goals, strategy, and best practices for leveraging data.
RapidMiner offers a full data science toolbox—combining data prep, machine learning, and model deployment and operations—that will empower all members of your team to get started on data science work.
And if you already have a data scientist or data science team on board, we can make sure that those resources are more productive by allowing cross-collaboration between coders and non-coders with RapidMiner’s visual workflow designer that accelerates the end-to-end machine learning process.
Download RapidMiner Studio which includes all of the capabilities to support the full data science lifecycle for your enterprise.
New to RapidMiner? Here's our end-to-end data science platform.
Additional Data Science Resources. Take a Look!
Whether you’re new to data science or extremely experienced – mistakes happen. Here we’ll look at some of the most common data science mistakes and how to avoid them.