Information Science and Data Science

Information science students and professionals learn to use a variety of tools and methods to organize, process, analyze, and visualize data. They also track emerging technologies and ensure they meet the information needs of users.

Combining math and statistics, specialized programming, advanced analytics, and AI/machine learning, data scientists uncover actionable insights that improve business operations and strategic planning.

What is Information Science?

Information science is a field that examines how people work with information and data. It incorporates concepts from library science, cognitive sciences and a host of other disciplines.

It is often compared to information theory, but it is a much broader field. Information science also overlaps with computer science, which tends to focus on more advanced computational skills.

A degree in information science can open the door to careers in a number of different industries. You can find jobs as a data scientist, knowledge manager or information architect. The flexible structure of this major allows students to create a program that best suits their interests and career goals. The BS in information science major includes a capstone experience that gives students the opportunity to apply their skills in real-world situations.

What is Data Science?

Data science is an interdisciplinary field that utilizes statistics, scientific computing, and other mathematical, computational and engineering disciplines to unlock insights and understanding from structured and unstructured data. It combines traditional areas of study such as information science, computer science and mathematics with a variety of contemporary topics including machine learning, network science, and computational neuroscience.

The field uses statistical analysis to uncover trends and patterns, automate processes, make predictions, and drive decision-making across multiple industries. Examples include a healthcare company using data science to identify potential drug side effects, logistics companies optimizing routes with data-backed forecasting tools, and an electronics firm leveraging machine learning to develop 3D-printed sensors for driverless cars.

Many of these applications rely on interpreting and relaying insights to non-technical business leaders and busy executives. This requires a high level of communication and a comfortable working with various programming languages and data visualization tools.

What is Big Data?

Big data refers to large, hard-to-manage volumes of structured and unstructured data that inundate businesses on a daily basis. These sets of information are generated by transactional applications, sensors and smart devices (IoT), social media and web activity, videos, audio, networks, log files and other business processes.

The main characteristics of big data are volume, velocity and variety. Volume is the amount of data – typically measured in terabytes, petabytes or even zettabytes – and velocity is the speed at which it comes in, such as streaming social media feeds or real-time data from sensor-enabled equipment.

Variety is the number of different data types and sources. It can include anything from text-based customer sentiment gathered through social media to test results from analyzing a series of CAD images that illustrate how minor changes to a product or process could affect performance.

What is Data Analytics?

Data analytics is a process for extracting insight from large datasets using statistical analysis, predictive modeling and AI. These insights are used to create data-driven products and drive decision-making across industries.

There are four types of data analytics: descriptive, diagnostic, predictive and prescriptive. Descriptive analytics focuses on summarizing trends in the data and is often the first step in a larger data analytics project. Diagnostic analytics aims to identify root causes by exploring correlations in the data. Predictive analytics predicts future outcomes based on historical trends and may use machine learning models. Prescriptive analytics provides a range of possible solutions to help companies automate decision-making and speed up approval processes.

Data scientists typically work with a team to prepare, integrate and analyze data. They may also need to communicate their findings with business stakeholders in a clear and effective way.

What is Data Management?

As data environments become increasingly complex, it is becoming more important for organizations to have effective data management. Ideally, this process will be a combination of business and IT teams working together to automate processes and ensure that information is available when needed.

Data management includes the processes and functions of cataloging, governing, cleaning and storing data within the business. It also involves ensuring that information is consolidated and easily accessible to all stakeholders.

This includes creating a data architecture, building and maintaining a database or data warehouse, and developing and managing a data analytics environment. It also involves defining what data is used for, and establishing standards that help maintain consistency across business systems. It can also include a DataOps approach, which is a collaborative process that brings people together from various groups to improve communication and accelerate development of data pipelines.

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