What is Data Science?
At first glance, the term “data science” can be somewhat intimidating and confusing. Doesn’t all science use data? Like many career paths, it can be difficult to understand what is involved in data science until you’ve taken a much closer examination of the field. For example, what kind of problems can you solve with a background in data science? What kinds of skills do you need to enter a career in data science? And why study data science at all? Let’s explore some of the answers to those questions, as well as the theories behind the growing field of data science.
Data Science for Beginners
Due to the complexity of the field, a simple data science definition might not be very illuminating. In short, data science involves using statistical analysis to study and sort large amounts of raw data. Ordering data allows us to draw meaning from it, and transforming it into easily understood charts of graphs makes it accessible to a non-technical audience.
But why bother sorting data to begin with? Renowned intellectual Noam Chomsky once succinctly described the problem while speaking on the subject of Internet research. He noted the value of using the internet for research — but only if you know what you’re looking for:
“If you have a framework of understanding which directs you to particular things, and sidelines lots of others—then this can be a valuable tool. Of course, you always have to ask yourself, ‘Is my framework the right one?’ Perhaps you need to modify it from time to time.”
This same problem exists within virtually any analytical career, including a path as a data scientist. The challenge of working with raw data is that you need imagination to turn it into something greater than what the numbers alone are showing you. Data involves analyzing convergent systems, similar to studying economics, business, sociology, or psychology.
Data scientists will also need to learn to work with “unstructured data”; content that doesn’t easily fit within a simple table. For example, the content of social media posts or online customer reviews. Because it’s not as simple as recording a number, it requires insight developed on your own to manipulate unstructured data. And you need a number of technical and non-technical skills to achieve that.
What is Data Science Used For?
The Internet has made it easy to collect an enormous amount of information. In fact, humans are collecting and storing more data than ever before in history. One estimate suggests there are 2.5 quintillion bytes of data created every day, a rate of growth that is only accelerating as web connectivity reaches more-and-more devices.
But unsorted, raw data is meaningless information. Acquiring and analyzing data requires you to determine which parts are pertinent and which are not — and that process transforms raw figures into actionable information. In practice that might mean finding new ways to reach audiences for marketing companies, creating more accurate policy modeling for insurance companies, and so on.
Data science achieves those objectives with a number of techniques and sub-fields. For example, data mining involves looking for patterns within data to help make predictions about future outcomes. It’s an overlapping intersection of machine learning, statistics, and big data. And it can be applied to countless purposes, from cutting costs to finding new ways to improve customer relationships.
Data modeling involves figuring out the most logical way to store data within a database. As different people need to manage and edit the data, the relevance of data can vary between users. Your database may also need to interact with other information systems. This kind of modeling can be essential for planning and communicating with those who never work directly with data.
Another factor to consider is that nearly any time a large set of data exists, there will be privacy concerns that go along with it. Understanding how to store and process large quantities of data can be valuable in a number of circumstances, which can make data management particularly critical for a variety of security reasons. But what kind of skills are necessary to enter these kinds of careers?
Data Science Skills
Those planning to enter the field of data science will want to hone various hard skills associated with this up-and-coming career path:
- Computer Science & Statistics / Data Methodology: Most data science careers will depend on a background in computer science and statistics. Data scientists use categorical data methods to solve problems on a regular basis. People who work in this field will need to learn the rudiments of machine learning and AI, as well as several programming languages like R programming, Python, Apache Spark, and SQL Database Coding. Depending on the data, data scientists may also need to use multiple applications to help map their results.
- Data Visualization: Data science careers often depend on learning about data visualization, so you can translate the data into something appreciable to a non-technical audience. If your job is finding ways to help a business by looking at Big Data, then it will help to have a better understanding of how businesses operate. And if you’re interpreting and modeling human data, then a better understanding of psychology can be essential to your success.
Outside of hard-skills, there are also a number of important soft skills, including a natural sense of curiosity, a collaborative nature, and attention to detail. In September 1999, the Mars Climate Orbiter crashed into the surface of Mars because of a mistake converting between the metric system and the imperial system of measurement. These are the kinds of events that occur when large numbers of people work together on a complex project, and a single member of the team misunderstands what’s going on.
It’s not uncommon to have teams of 8-15 data scientists working to create a single spreadsheet, or two teams of data scientists working on overlapping problems. When miscommunications happen between the data-driven interactions of various members of a team, the results can be bad for everyone. In short, teamwork and communication will be essential skills for virtually any data science career.
Data Science Curriculum
At the undergraduate level, someone preparing to enter data science might take courses like:
- STAT 135. Concepts of Statistics
- COMPSCI 186 or W186. Introduction to Database Systems
- COMPSCI 189. Introduction to Machine Learning
- STAT 102 Data, Inference, and Decisions
- INFO 159. Natural Language Processing
- STAT 158. The Design and Analysis of Experiments
However, you can acquire those kinds of skills across many different kinds of programs. The importance of data science programs becomes more significant at the graduate level. For an advanced degree, a data science curriculum might include:
- ANA 600 Fundamentals of Analytics
- ANA 605 Analytic Models & Data Systems
- ANA 610 Data Management for Analytics
- ANA 615 Data Mining Techniques
- ANA 620 Continuous Data Methods, Applied
- ANA 625 Categorical Data Methods, Applied
- ANA 630 Advanced Analytic Applications
National University’s unique Data Science program makes it possible for to further specialize your education with courses like:
- BAN 650 Probabilistic Finance Models
- BAN 655 Analytical Security & Ethics
- ANA 655 Data Warehouse Design & Development
- ANH 604 Clinical Research Analytics
- ANH 607 Health Outcomes Research
Why Data Science?
Because so many different levels of expertise are required for these kinds of positions, data science jobs tend to have very rewarding salaries. The Bureau of Labor Statistics estimates the average salary for these careers to be $122,840 annually. Glassdoor provides a slightly lower figure of $113,309 per year.
The somewhat extensive knowledge and experience requirements make data scientists in high demand. The BLS also estimates 15% expected industry growth for computer and information research scientists over the next decade, far ahead of the average for all occupations. And experience in these positions, especially when paired with a graduate degree, provides a wealth of opportunities for career advancement.
Financial reasons aside, there are many appealing things about data science. It can always provide a new and unique challenge to solve. As data is collected in virtually every industry, data scientists have the opportunity to work in a variety of different areas, and continue to learn about new things long after leaving formal education. People who thrive on curiosity may also appreciate the interconnected relationship with other academic fields.
Learn Data Science at National University
National University is a regionally accredited school that offers a Master of Science in Data Science. Students learn to evaluate data management methods and construct data programming techniques to circumstance-appropriate problem solving using data analytics. Graduates are prepared to enter a variety of data science careers, and thrive in those positions.
If you’re considering pursuing a career in data science, get in touch with a member of our admissions team today to learn more about educational opportunities available to you and how to delve further into an intriguing career path.