1.2 Computer Science across the Disciplines

Data Science

Across business, financial, governmental, scientific, and nonprofit workplaces, millions of people are programming, and most of the time, they don’t even know it! A spreadsheet is an example of a data-centric programming environment where data is organized into cells in a table. Instead of presenting programs as primarily about algorithms, spreadsheets present programs as primarily about data. Spreadsheets are often used for data analysis by offering a way to organize, share, and communicate ideas about data. Spreadsheets are uniquely effective and accessible because they allow for the visual organization of data in whichever structure makes the most sense to the user. Instead of hiding data behind code, spreadsheets make data as transparent and up to date as possible.

Although spreadsheets make computation accessible for millions of people across the world, they have several shortcomings. Unless limits are removed, many popular spreadsheet software products such as Microsoft Excel may have a limitation to the number of rows of data they can store that is less than the data of modern computers. One such example occurred in October 2020 when Public Health England failed to report 15,841 positive cases of COVID-19 in the United Kingdom due to mismanaged row limits in the spreadsheet used. This shortcoming attests not only to the technical limit on the number of rows supported by spreadsheets, but also to the design limitations of software that fails to communicate data loss, irregularities, or errors to users. Errors in spreadsheet software data entry can often go unnoticed because spreadsheets do not enforce data types. Cells can contain any content: numbers, currencies, names, percentages, labels, and legends. The meaning of a cell is determined largely by the user rather than the software. Spreadsheets are an expressive and accessible technology for data analysis, but this creative power that spreadsheets afford to users is the very same power that limits spreadsheets as a data management and large-scale data analysis tool. The more data and the more people involved in a spreadsheet, the greater the potential for spreadsheet problems.

The interdisciplinary field that applies computing to managing data and extracting information from data is called data science. Data scientists are practitioners who combine computing and data analysis skills with the domain knowledge specific to their field or business. The demand for data scientists is becoming increasingly important as more and more research and business contexts involve analyzing big data, or very large datasets that are not easily processed using spreadsheets. These datasets often involve high-volume measurements of user interactions on the Internet at a very fine grain, such as tracking a customer’s web browser history across an online storefront. Data scientists can then analyze browser patterns using machine learning methods in order to recommend related products, target advertisements to specific customers over social media, and reengage customers over email or other means. Machine learning (ML) is a subset of artificial intelligence that relies on algorithms and data to make it possible for artificial intelligence to learn, actually mimicking the way humans learn. For example, ML is used to identify fraudulent versus legitimate banking transactions. Once a computer learns how to distinguish fraudulent transactions, it can be alert and call attention to suspicious banking activity.

Computational Science

Beyond data science, computer science can also fundamentally change how science is researched and developed. The field of computational science refers to the application of computing concepts and technologies to advance scientific research and practical applications of scientific knowledge in a wide range of fields, including civil engineering, finance, and medicine (among many others). For example, algorithms and computer software play a key role in enabling numerical weather prediction (Figure 1.7) or the use of mathematical models to forecast weather based on current conditions in order to assist peoples’ everyday lives and contribute to our understanding of the climate models, climate changes, and climate catastrophes. These algorithms may rely on a large amount of computer hardware power that might not be available in a single system, so the work may need to be distributed across many computers. Computational science studies methods for realizing these algorithms and computer software.

Figure 1.7 Meteorologists collect data from a variety of sources and use the data, algorithms, and computers to predict the weather. (data source: Climate Forecast System, National Centers for Environmental Information, National Oceanic and Atmospheric Administration, https://www.ncei.noaa.gov/products/weather-climate-models/climate-forecast-system; credit: modification of "CFSR Atmospheric Precipitable Water" by NOAA/ncei.noaa.gov, Public Domain)

Although computer science has been used to support scientific discovery, the theory of knowledge of computer science has historically been considered quite different from that of the natural sciences, such as biology, physics, and chemistry. Computer science does not study natural objects, so to most, it would not be considered a natural science but rather an applied science. Unlike natural sciences such as biology, physics, and chemistry, which emphasize the discovery of natural phenomena, computer science often emphasizes invention or engineering.

However, computer science is today deeply interdisciplinary and involves methods from across science, mathematics, and engineering. Computer scientists design, analyze, and evaluate computational structures, systems, and processes.

• Mathematics plays a key role in theoretical computer science, which emphasizes how a computational problem can be defined in mathematical terms and whether that mathematical problem can be efficiently solved with a computer.
• Engineering plays a key role in software engineering, which emphasizes how problems can be solved with computers as well as the practices and processes that can help people design more effective software solutions.
• Science plays a key role in human-computer interaction, which emphasizes experimentation and evaluation of the interface (boundary) between humans and computers, often toward designing better computer systems.

Information Science

Not only is computation interdisciplinary, but other disciplines are also becoming more and more computational. In The Invisible Future, Nobel Laureate biologist David Baltimore defines DNA in computational terms. He states that biology is an information science because DNA encodes for the outputs of biological systems. The interdisciplinary field studying information technologies and systems as they relate to people, organizations, and societies is called information science. The role of information in natural sciences can also be found in the physics of quantum waves that carry information about physical effects, in the chemical equations that specify information about chemical reactions, in the information flows that drive the evolution of economies and political organizations, and in the information processes underlying social, management, and communication sciences.¹³

Although information science has its roots in information classification, categorization, and management in the context of library systems, information science today is a broad field that encompasses the many diverse ways information shapes society. For example, today’s social media networks provide more personable and instantaneous information communication compared to traditional news outlets—billions of people around the world are using social media to engage with information about the world. For many people, social media may be the primary way that they learn about and make sense of the world (Figure 1.8). Yet, we’ve already seen risks associated with information technologies such as the Internet. In today’s “information age,” information has more power than ever before to reshape society. Information scientists, data scientists, computational scientists—and, therefore, computer scientists—have a social responsibility: “One cannot reap the reward when things go right but downplay the responsibility when things go wrong.”¹⁴

Figure 1.8 While Americans used to primarily get their news from newspapers, as technology has advanced their primary source of news media has shifted. As of 2020, 53% of American adults surveyed stated they got their news from social media at least some of the time. (data source: Elisa Shearer and Amy Mitchell, Pew Research Center. "About Half of Americans Get News on Social Media at Least Sometimes." From Survey of U.S. adults conducted Aug. 31–Sept. 7, 2020. In: E. Shearer, A. Mitchell, "News Use Across Social Media Platforms in 2020," Jan 12, 2021.; attribution: Copyright Rice University, OpenStax, under CC BY 4.0 license)

Despite the centrality of information to decision-making and social change, dominant approaches to computer science tend to focus on computational structures, systems, and processes (such as algorithms) that describe one kind of information by focusing on the what or how of solving problems with computers, but less often the why or who questions. Information science broadly centers people, organizations, and society in the study of information technologies.

Computer Science Is an Interdisciplinary Field

In presenting data science, computational science, and information science, we’ve introduced the idea that computer science can shape other disciplines. But we’ve also raised questions about what computer science is today. If computer science is the study of all “phenomena surrounding computers,” it could also involve data science, computational science, bioinformatics, cheminformatics, computational social science, medical informatics, and information science. As you will learn in Chapter 13 Hybrid Multicloud Digital Solutions Development, another aspect of computer science is responsible computing, which includes the appropriate management of cyber resources as well as robust cybersecurity. It is difficult to define computer science today because it is so widely used by people across the world in diverse capacities. Definitions are about defining boundaries and excluding practices, which may be helpful for understanding the practices of a certain culture or group that is “doing” computer science, but it can never truly represent everyone and all the things that people are doing with computer science. However, computer science’s historical roots in mathematics shape the way it categorizes subfields:

• Theoretical computer science
  • Theory of computation
  • Information representation
  • Data structures and algorithms
  • Programming language and formal methods
• Computer systems
  • Architecture
  • Artificial intelligence
  • Networks
  • Security
  • Databases
  • Distributed computing
  • Graphics
• Applied computer science
  • Scientific computing
  • Human-computer interaction
  • Software engineering

In this hierarchy, theoretical computer science and computer systems are treated separately from applied computer science and human-computer interaction, suggesting that the mathematics of computing are pure and separate from social questions. Yet we’ve seen several examples that question this paradigm and instead point to a structure where human-computer interaction is infused throughout the study of computer science and all its subfields.

Today, computer science is a field that is just as much about people as it is about computer technology because each of these subfields is motivated by the real-world problems that people ultimately want to solve. The subfields of artificial intelligence and machine learning have applications that directly influence human decision-making, ranging from advertisement targeting to language translation to self-driving cars. Effective computational solutions to research or business problems require combining specific knowledge with computer science concepts from a combination of areas. For example, the computational science application of weather prediction combines knowledge about various subfields of computer science (algorithms, distributed computing, computer systems) with knowledge about climate systems. Theoretical computer scientists are increasingly interested in asking questions such as, “How do we design, analyze, and evaluate algorithms or information systems for fairness? How do we even define fairness in a computer system that strips away the complexities of the real world? What ideas or information are encoded in the data? And what are the limits of our approaches?” Computer science is a complex field, and its synergistic nature means that when computer science is used in an interdisciplinary manner that shapes other disciplines, its impact on society is much greater than when each discipline functions on its own.