All About Data Science
In today’s digital age, data is given and collected during numerous activities and interactions. From the information we input into our personal devices to census surveys we fill out for the government, it is easier than ever to find and analyze data for various purposes; and so has emerged the field of Data Science, an interdisciplinary field at the crossroads of mathematics, technology, and science. Read on to find more about the history, careers, and future of this particular STEM-related field!
Photo is courtesy of Energepic via Pexels.
The History of Data Science
Before the field of Data Science was created, it was known as the study of statistics, according to Forbes. This includes activities such as finding patterns in data, harvesting useful information, as well as processing the data. Then, in the early 2000s, the word “Data Science” began to be utilized in various journals and publications, as a method “to enlarge the major eras of technical work the field of statistics,” according to a piece written by William S. Cleveland titled, “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” The actual term, however, has been accredited to DJ Patil and Jeff Hammerbacher, two data scientists, as the University of Wisconsin explains. This new field created a much-needed intersection between the current study of statistics and the evolving area of computer science. And so, this newfound subject has been since gaining popularity and usefulness in our data and technology-driven world today.
What is Data Science?
Data Science involves data and technology, but what actually is it? The Berkeley School of Information states that in 2009, a definition for this term was coined by Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics. It is as follows: Data Science is, “The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.” As a result, Data Science allows people to collect data and apply it to understand and solve real-world problems. There are a couple of steps as part of the Life Cycle of Data Science: Capture, Maintain, Process, Analyze and Communicate, each of which has its own separate activities.
Image is courtesy of Berkeley School of Information.
Careers in Data Science
With the onset of Data Science has come a new set of careers. As it is an up-and-coming field, there are many benefits to looking for a job opportunity within it, such as honing 21st-century skills and being able to solve complex problems, amongst others. edX Blog suggests that there are two main tracks to having a career in Data Science: diving straight into being a Data Scientist or taking on the role of an Analyst.
Data Scientists are the ones who collaborate hands-on with raw data and identify patterns and pertinent information. This involves a good check of research and modeling, along with using various software and technologies. Some core data scientist skills include data visualization, machine learning, programming, statistics, and understanding big data.
On the other hand, data analysts are tasked with applying data collected to answer questions, and in turn, solve problems. Instead of predicting future trends, they use current and historical data when carrying out their work. Some core data analysts’ skills include Excel, data visualization, programming, and reporting.
Furthermore, beyond the role of data scientist and analyst, more specialized roles have emerged as well. According to Discover Data Science, these include:
Data Engineers, who are the builders of data infrastructure and assist in the creation of information systems. They are well-versed in various programming languages and have multiple years of experience within the field of software engineering.
Business Analysts, who are more knowledgeable about the different business processes and strategies. Their skills include using data visualization tools and data modeling and usually have an interest in project management and business development.
Data Mining Specialists, who are responsible for analyzing and identifying specific patterns within data in order to predict future trends and current relationships. They use various research and software skills to carry out their tasks.
Machine Learning Engineers, who work with big data applications and software programming in order to create methods for computers to collect data from real people through technologies.
Database Developers, who create and implement new databases. This involves information storage and data mining operations.
The Future of Data Science
Analytics Vidhya shows that with the advancement of Data Science has come the opportunity for it to integrate into other fields, to use data-driven methods and technologies to carry out their own respective projects. For example, in healthcare, Data Science has allowed hospitals to create comprehensive databases of patients and their health-related information, which has allowed for specialists to identify early onsets of specific diseases, and create resources to share with institutions across the world, such as organ donor databases. Another example is the field of banking and finance, where financial security has increased. Here, Data Science can be used to identify fraudulent activities, as well as providing predictions as to where to invest one’s money. Finally, cryptocurrency is a major Data Science-related innovation, as it has created a need to manage data through online means in a secure and comprehensive manner.
Overall, it is evident that there is much to come for Data Science. From human-centered data collection to machine learning interfaces, there are many ways for you to use your own current expertise or gain a new skill when it comes to this field!
Explore Data Science
Does Data Science sound like an interesting field to you? What to learn more about this up-and-coming field? There are various online courses that teach basic data collection and programming skills related to this field. Below are some suggestions (in no way endorsed by R2AC):
Article Author: Asima Hudani
Article Editor: Valerie Shriobokov, Victoria Huang