Research and Education in Data Science: Challenges for the Area of Information Systems

Abstract

Data Science (DS) is an interdisciplinary research area that uses concepts based on Big Data Analytics, Programming Languages and Mathematical fundamentals to develop research into insight discovery from datasets. The data-driven decision-making approach has become a challenge for researchers and lecturers in the Information Systems area. This is because the skills and theoretical issues require a heavy course workload and high number of class hours. This work introduces a discussion of the insertion of the DS subject in Information Systems courses, and researchers' efforts to establish goals for professionals and lecturers involved in DS. IntroductionThe proliferation of computational systems in the industry has caused significant changes in the way data are collected, transmitted and analyzed [Turban et al. 2010]. In addition, there is considerable progress in the management of persistent systems and Data Base Management System (DBMS). The advance started with Peter Chen [Chen 1976] who proposed the E-R diagram to aid the design of databases and defined the requirements of problems, entities, attributes and relationships in a graphical way. Complementarily, DBMS has been consolidated as the main database approach, ensuring the properties such as Atomicity, Consistency, Isolation and Durability, called ACID [Silberschatz et al. 2006].Relational DBMS was the major resource to store data, facts or transactions, and it became a valuable tool for data analysis using tables, reports, maps or graphs, allowing approaches to manage or to monitor business in most areas of knowledge.Data generation has been growing exponentially and, which demands collecting, organizing, analyzing and extracting insights from data warehouses systematically, in heterogeneous environments, geographically distributed and, in distinct contexts of applications.