Best Practices in Accelerating the Data Science Process in Python

The number of data science and big data projects is growing, and current software development approaches are challenged to support and contribute to the success and frequency of these projects. Much has been researched on how data science algorithm is used and the benefits of big data, but very little has been written about what best practices can be leveraged to accelerate and effectively deliver data science and big data projects. Big data characteristics such as volume, variety, velocity, and veracity complicate these projects. The proliferation of open-source technologies available to data scientists can also complicate the landscape. With the increase in data science and big data projects, organizations are struggling to deliver successfully. This paper addresses the data science and big data project process, the gaps in the process, best practices, and how these best practices are being applied in Python, one of the common data science open-source programming languages

A review and future direction of agile, business intelligence, analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions

The Relationship of Character Strengths to Sexual Behaviors and Related Risks among African American Adolescents

Background: Relatively few studies have examined positive character traits that are associated with lower HIV/STD risks. Purpose: In the present study, the relationships of character strengths with sexual behaviors and attitudes were assessed among 383 African-American adolescents. Method: Character strengths were measured using the Values in Action Inventory of Strengths. Because the strengths were highly inter-correlated, stepwise discriminant function analyses were utilized to assess their independent associations with sexual behaviors and attitudes. Results: Greater Love of Learning was related to self-reported abstinence from sexual intercourse for boys and self-reported abstinence from drug use for boys and girls. Greater Love of Learning and Curiosity were related to the belief in no premarital sex for boys, whereas only Curiosity was significant for girls. Prudence was related to reported abstinence from sexual intimacy (e.g., touching or kissing). Judgment was related to sexual initiation efficacy for girls and boys, whereas Leadership was only significant for girls. Conclusions: The findings suggest that character strengths may be associated with lower levels of sexual behaviors and sex-related beliefs among a sample of African-American adolescents