Science is the study of the nature, processes and phenomena around us. It includes both natural sciences as well as social sciences.
Science literature is an invaluable source of data for Wikipedia articles and editors can use it as fuel for discussion on the talk page. But verifyability is paramount when creating such content.
Information science is the study of information, its collection, organization, storage and retrieval. It draws from multiple disciplines such as library science, computer science and linguistics.
Information can be stored and processed in many ways, from verbal to visual to mediated. As an information science major, you’ll gain the skillset needed to manage information life cycles and create technology-based systems that improve people’s lives.
The field of information ethics and architecture spans a multitude of topics. You’ll gain knowledge on creating systems that are equitable, accessible, and uphold human values.
An information scientist is an individual with a relevant degree and professional experience who specializes in providing focused data to scientific or technical research staff in industry or subject faculty and students at universities. Academic information scientists typically hold either a master’s degree in the field or an advanced degree (MLS/MI/MA in IS) related to information and library studies.
Data science is the study of big data sets and how to extract meaningful insights from them using statistics, machine learning, pattern recognition and other techniques. This interdisciplinary field tackles problems across numerous domains such as business analytics, marketing optimization, risk management, agriculture, public policy issues, fraud detection methods, healthcare services and public transport services.
Data scientists must possess both technical and soft skills, such as data preparation, mining, predictive modeling, statistical analysis, computer science, programming languages, mathematics and subject expertise. Furthermore, they need to communicate their findings in an easily understood manner by business users.
Data science ultimately strives to help businesses succeed. They analyze and anticipate customer behavior and trends to spur sales, boost revenue and reduce expenses. Furthermore, data scientists can detect fraud and optimize processes for improved efficiency within a business.
Machine learning is an approach to artificial intelligence (AI) that utilizes algorithms to gain insight from data and make predictions. It has applications in numerous fields, from healthcare and finance to computer gaming.
Technical research is constantly progressing to create algorithms that can analyze and predict data. It is one of the fastest-growing areas in computer science, offering us more evidence-based decision making so we make better informed choices. This field has the potential to transform our lives for the better by fostering innovation within existing infrastructures.
Machine learning is becoming more widely employed across a range of disciplines, and science is no exception. At the DOE Office of Science, scientists are employing machine learning techniques to analyze vast amounts of data generated from user facilities like particle accelerators and X-ray light sources.
Machine learning is a rapidly-evolving trend in healthcare. It can analyze medical images to help doctors detect illness or anticipate treatment outcomes. Furthermore, machine learning can be employed to automate routine tasks to reduce human error.
Artificial intelligence (AI) is the ability of machines to learn and make decisions with human-like levels of understanding. It uses multiple technologies that give machines the capacity to perceive environments, recognize objects, contribute to decision making, solve complex problems, and draw upon past experiences.
AI also utilizes techniques to represent and translate information, such as knowledge representation and knowledge engineering. These enable AI programs to comprehend and utilize various types of data such as images or text.
The AI world is revolutionizing how we interact with technology and each other, as well as how society and economies function. People will need new skills such as critical thinking, collaboration, design and visual display of data.
Governments should encourage the development of trustworthy AI systems that address ethical concerns and minimize harmful bias. Current laws pertaining to discrimination in physical economies need to be extended to digital platforms. It makes more sense to focus on broad objectives and implement policies that further them rather than trying to decode individual algorithms’ inner workings.