A Behind the Scenes Look at Data Science: Definitions, Applications, History, Skills, Salaries, and More
Today’s pop quiz: What is data science?
Data science is:
- The practice of working with data to generate valuable business insights and solve real-world problems
- A booming field that is driving innovation and change across nearly all industries
- An in-demand profession that commands salaries well above $100,000
- All of the above
If you answered “All of the above,” congratulations — you are correct!
Here’s a more detailed definition of data science, excerpted from Techopedia.com:
Data Science is a broad field that refers to the collective processes, theories, concepts, tools, and technologies that enable the review, analysis, and extraction of valuable knowledge and hidden information from raw data.
Data science enables the use of theoretical, mathematical, computational, and other practical methods to study, evaluate, and model data. It is geared toward helping individuals and organizations make better decisions from stored, consumed, and managed data.
Read on for a comprehensive look at:
- Game-changing applications of data science
- History of data science
- Key related disciplines like artificial intelligence and machine learning
- Skills needed to succeed in this exciting, high-paying field
- The employment and career outlook
- And more
Applications of Data Science [Impact Across all Industries]
Data scientists are having an impact in almost every industry.
- Health care
- eCommerce
- Law enforcement
- Marketing/advertising
- Transportation
- Sports
As expected, different sectors are using data science in different ways. Here are some examples from technology information networking site BuiltIn.com:
Health care:
- Identifying and predicting disease
- Personalized health care recommendations
eCommerce
- Automated “smart” ad placement
- Personalized product recommendations
Law enforcement
- Data-driven crime predictions
- Facial recognition tools
- Tax fraud enforcement
Transportation
- Optimized shipping routes
- Modeling the most effective traffic patterns and streetlight usage
- Getting hot food delivered quickly
Chances are your favorite sports team may be dabbling in data science to help put together the best, most cost-effective team.
Why, data science is even at the heart of helping people find love — through online dating platforms powered by complex algorithms.
Quick History of Data Science & Big Data
A Forbes article titled “A Very Short History Of Data Science” is one of many sources to cite a 1962 text by mathematician John Tukey as the first to suggest that the emerging field of data analysis was “intrinsically an empirical science” — a previously unrecognized scientific field whose subject of interest was learning from data.
The term “data science” has been traced to 1974, when Danish computer science pioneer Peter Naur proposed it as an alternative name for computer science. The term “data science” is used throughout his book “Concise Survey of Computer Methods,” though his definition of it could be considered a bit of a head-scratcher. He described data science as: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”
For a more complex look at the history — one in which the essential components of data are traced back to pre-1800s — industry resource KDnuggets.com offers a “History of Data Science Infographic in 5 Strands” connecting the fields of:
- Computer Science
- Data Technology
- Visualization
- Mathematics
- Statistics
Why is Data Science Important?
Big Data may have the potential to change the world for the better but data science is essential because, according to training provider SimpliLearn, because “without the expertise of professionals who turn cutting-edge technology into actionable insights, Big Data is nothing.”
In “Why Data Science Matters And How It Powers Business Value,” the company details eight ways that data scientists can add value to business.
- Empowering management to make better decisions
- Directing actions and defining goals based on trends
- Challenging staff to adopt best practices and focus on issues that matter
- Identifying business opportunities
- Decision making with quantifiable, data-driven evidence
- Testing these decisions
- Identifying and refining of target audiences
- Recruiting the right talent
Data science has the potential to help nearly all organizations, according to Damien Deighan, CEO of Data Science Talent.
“With the ability to uncover hidden patterns, unknown correlations and build models that can make accurate predictions, data science can be used to help you make better business decisions for your organization,” says Deighan. “You can now analyze almost anything and everything in relation to your organization. Anything that can be logged via computer or network use can be analyzed and organized and turned into actionable insights. When applied and used correctly, data analytics can play a pivotal role in driving profitability and productivity.”
Key Areas of Data Science
The field of data science encompasses multiple subdisciplines such as data analytics, data mining, artificial intelligence, machine learning, and others.
Data Analytics
While data analysts are focused on extracting meaningful insights from various data sources, data scientists go beyond that to “forecast the future based on past patterns,” according to SimpliLearn. “A data scientist creates questions, while a data analyst finds answers to the existing set of questions.”
Artificial Intelligence
Commonly called AI, artificial intelligence, according to Techopedia, “aims to imbue software with the ability to analyze its environment using either predetermined rules and search algorithms or pattern recognizing machine learning models, and then make decisions based on those analyses. In this way, AI attempts to mimic biological intelligence to allow the software application or system to act with varying degrees of autonomy, thereby reducing manual human intervention for a wide range of functions.”
Machine Learning
Machine learning algorithms use statistics to find patterns in massive amounts of data, according to MIT Technology Review. A subdiscipline of AI, “machine learning is the process that powers many of the services we use today — recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.”
What Does a Data Scientist Do?
Data scientists “crack complex data problems with their strong expertise in certain scientific disciplines,” including mathematics, statistics, computer science, and more, according to online learning platform Edureka.
A post by the tech-focused venture capital firm Sequoia Capital on the impact of data science describes two main camps of data scientists:
- Product analysts, whose role is to deliver data-informed stories that advocate for a change in product or strategy.
- Algorithm developers, whose role is to incorporate data-driven features into products (e.g., optimizing recommendations or search results).
DataJobs.com describes data scientists as being focused on two primary areas:
- Discovery of data insight — “Diving in at a granular level to mine and understand complex behaviors, trends, and inferences … surfacing hidden insight that can help enable companies to make smarter business decisions.”
- Development of data product — “The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data.” Other examples include Gmail’s spam filter and computer vision used for self-driving cars.
What Skills do Data Scientists Need?
According to “Build a Career in Data Science,” an eBook by technology educator Manning Publications, the required skill sets for data scientists are often broken down into three key areas:
- Mathematics/statistics (to understand what types of analysis is possible and the techniques involved)
- Databases/programming (to write code that produces the algorithms that do the work)
- Domain knowledge/business understanding (to optimize the technical work referenced above to the unique conditions of the organization you are working for and the business domain you are operating in)
Any review of data science skills also distinguishes between the technical hard skills that are needed and the so-called soft skills.
Though many data science training programs focus primarily on high-tech hard skills, “industry data, market trends, and insights from top business leaders highlight soft skills as a key component to success in the workplace” as well according to KDnuggets.com, which enumerates the following key skills in “These Data Science Skills will be your Superpower.”
Hard Skills
- Mathematics and statistics skills
- Essential programming skills (including Python and R)
- Data wrangling and preprocessing skills (because “the predictive power of a model depends on the quality of the data that was used in building the model”)
- Data visualization skills (ability to use data visualization packages, such as matplotlib, seaborn, and ggplot2)
- Basic machine learning skills (problem framing, data analysis, model building, testing & evaluation, and model application)
- Skills from real-world capstone data science projects (demonstrate evidence of successful completion of a real-world data science project)
Soft Skills
- Communication skills (to work well with team members and to present complex information to stakeholders who are unfamiliar with technical data science concepts)
- Lifelong learning (because data science is continually evolving)
- Business acumen (knowledge about your organization’s goals and the business environment in which it operates)
- Ethics (data scientists have the responsibility to avoid manipulating data or using a method that will intentionally produce bias in results)
Why Become a Data Scientist?
High salaries and high demand for your services are just part of the equation.
Data scientists are incredibly valuable to the organizations they serve. And since data science now touches nearly every industry, you’ll be well-positioned to land jobs in fields that are most interesting to you. This includes:
- Health care
- Financial services
- Cybersecurity
- Marketing
- Manufacturing
- Aerospace
- Media
- Government
- And many more
Data Scientist Career Paths
Data science is a diverse field with a wide variety of potential career paths. In addition to data scientist and data analyst, the following job titles are also in demand:
- Machine learning engineer
- Data architect
- Data engineer
- Business intelligence analyst
- Marketing analyst
- Statistician
- Quantitative analyst
[Webinar] WATCH: Data Science Careers Webinar [Key Info About an Extraordinary Opportunity] >>
Data Scientist Career Outlook
Career opportunities for data scientists continue to expand rapidly across a broad spectrum of fields. TechRepublic called it the “No. 1 most promising job in America” in 2019, citing a median base salary of $130,000 and a single-year increase in job openings of 56%. ZDNet reports data showing three-year hiring growth of 37% for data scientist jobs.
RELATED READING: Data Scientist Salaries — High Pay in an Exciting, In-Demand Field >>
The U.S. Bureau of Labor Statistics lists a median salary of $122,840 for data scientist and related positions (with the highest-paid 10 percent earning more than $189,780). The BLS projects job growth “much faster than the average for all occupations.” The need for data scientists is “growing exponentially,” according to TechTarget, which reports that “Demand for data scientists is booming and will only increase.”
Data Science FAQs
How important is a master’s degree when pursuing a career in data science?
You’ll see figures stating that at least 75% and perhaps as many as 94% of data scientists have earned a master’s degree or higher. In this field, a master’s degree is considered invaluable, if not essential, for both the theoretical and practical skills you will gain from the experience.
What are the most valuable skills for a data scientist?
Skills commonly deployed by data scientists include mathematics and statistics, machine learning, predictive modeling, data visualization, text mining, programming (including Python, R, SQL, Spark, Hadoop, Julia), and many more. Data scientists also need soft skills, especially oral and written communication, to present often complex concepts to stakeholders.
What are some of the top data science blogs to keep an eye on?
In addition to ongoing reporting on data science news and trends published by the University of San Diego’s online master’s degree program, here are several lists of leading data science blogs compiled by Medium.com, Tableau.com, and 365datascience.com.
Does the USD master’s degree program require an undergraduate degree in science or engineering?
Most applicants to USD’s Applied Data Science master’s degree program have an undergraduate degree in science, mathematics, engineering, information technology, computer science or a STEM field. However, the program is also open to those with a bachelor’s degree from other fields (such as business, for example); such applicants are asked to provide a written statement on how their skills and experience are suited to the program.
