Abstract
The potential wide applications of big data analytics have created a high demand for data analysts in various industries, including business, healthcare, bioinformatics, politics, and management. As a result, higher education institutions are capitalizing on this opportunity by offering different data science programs to attract students and cater to industry needs. Over the past decade, there has been a rapid emergence of data science programs both nationally and globally. This chapter will begin by reviewing the impact of big data analytics on different industries. It will then proceed to describe various data science programs, including their curriculum design, course offerings, and target industry sectors for employment. Additionally, the chapter will address the weaknesses of some curricula and propose new teaching areas that are relevant to improve the learning outcomes of students. The aim of the suggestions is to better prepare data science students for the ever-evolving demands of big data analytics in the industry.
TopBig Data Analysis And Its Impact
What is data? To answer this question, we need to define the concept of information. Let us say the world is made of entities. We human beings measure the properties of entities and observe the behaviors of entities. We understand the entities through observing and processing the information produced by the entity. The information could be in many different forms: electronic, acoustic, mechanic, chemical, biological, and sociological. It is worth noting that we might only be able to observe a fraction of the information produced by the entity. If we can collect more information, we can have a more accurate understanding of the entity (Seife, 2007; Gleick, 2012).
Key Terms in this Chapter
Data Science: A branch of computer science that utilizes mathematical analytical methods to study big data in order to retrieve useful information.
Big Data: Extremely large dataset that are generated in the digital age, which contains hidden useful information that can only be mined with modern computing technologies. Big data are usually unstructured have 5Vs property (volume, variety, velocity, value, and veracity).
Social Analytics: The application of data science technologies in social entities and social activities. Examples include web analysis, election analysis, and virtual network analysis.
Business Data Analytics: An interdisciplinary field that utilizes data science and data technologies in business practice to analyze customer data, product data, transaction data, in order to reduce cost, expend market, and improve customer experience.
Data Engineering: An interdisciplinary field that involves computer engineering, software engineering, artificial intelligence, and mathematics. The objectives of data engineering is to design, build, and integrate data processing programs and platforms that be used by data analysts.
Data Analytics: The practice of using data science and data engineering technologies to analyze real-world big data in order to answer real-world questions or solve real-world problems.
Curriculum Design: The practice to systematically organize curriculum within a program. Important curriculum design activities include course selections, scheduling, and evaluation.
Bioinformatics: The application of data science technologies in molecular biology, genetics, systems biology, and structural biology. The main objective of bioinformatics is to establish the correlation between genetic information and the behaviors and properties of organisms. The study in this field can facilitate the development of new drugs, gene alteration, organic farming, and synthetic meat.
Healthcare Data Analytics: The application of data science technologies in healthcare field. Through analyzing patient data and retrieving useful information, healthcare data analytics aims at improving patient service, reducing cost, promoting health, and preventing diseases.