There is a widely forecast skills gap developing between the numbers of Big Data Analytics (BDA) graduates and the predicted jobs market. Many universities are developing innovative programs to increase the numbers of BDA graduates and postgraduates. The University of Derby has recently developed two new programs that aim to be unique and offer the applicants highly attractive and career-enhancing programs of study. One program is an undergraduate Joint Honours program that pairs analytics with a range of alternative subject areas; the other is a Master's program that has specific emphasis on governance and ethics. A critical aspect of both programs is the synthesis of a Personal Development Planning Framework that enables the students to evaluate their current status, identifies the steps needed to develop toward their career goals, and that provides a means of recording their achievements with evidence that can then be used in job applications. In the UK, we have two sources of skills frameworks that can be synthesized to provide a self-assessment matrix for the students to use as their Personal Development Planning (PDP) toolkit. These are the Skills Framework for the Information Age (SFIA-Plus) framework developed by the SFIA Foundation, and the Student Employability Profiles developed by the Higher Education Academy. A new set of National Occupational Skills (NOS) frameworks (Data Science, Data Management, and Data Analysis) have recently been released by the organization e-Skills UK for consultation. SAS^® UK has had significant input to this new set of NOSs. This paper demonstrates how curricula have been developed to meet the Big Data Analytics skills shortfall by using these frameworks and how these frameworks can be used to guide students in their reflective development of their career plans.

Data scientists and analytic practitioners have become obsessed with quantifying the unknown. Through text mining third-person posthumous narratives in SAS^® Enterprise Miner™ 12.1, we measured tangible aspects of personalities based on the broadly accepted big-five characteristics: extraversion, agreeableness, conscientiousness, neuroticism, and openness. These measurable attributes are linked to common descriptive terms used throughout our data to establish statistical relationships. The data set contains over 1,000 obituaries from newspapers throughout the United States, with individuals who vary in age, gender, demographic, and socio-economic circumstances. In our study, we leveraged existing literature to build the ontology used in the analysis. This literature suggests that a third person's perspective gives insight into one's personality, solidifying the use of obituaries as a source for analysis. We statistically linked target topics such as career, education, religion, art, and family to the five characteristics. With these taxonomies, we developed multivariate models in order to assign scores to predict an individual's personality type. With a trained model, this study has implications for predicting an individual's personality, allowing for better decisions on human capital deployment. Even outside the traditional application of personality assessment for organizational behavior, the methods used to extract intangible characteristics from text enables us to identify valuable information across multiple industries and disciplines.

The experiences of the programmer role in a large SAS^® shop are shared. Shortages in SAS programming talent tend to result in one SAS programmer doing all of the production programming within a unit in a shop. In a real-world example, management realized the problem and brought in new programmers to help do the work. The new programmers actually improved the existing programmers' programs. It became easier for the experienced programmers to complete other programming assignments within the unit. And, the different programs in the shop had a standard structure. As a result, all of the programmers had a clearer picture of the work involved and knowledge hoarding was eliminated. Experienced programmers were now available when great SAS code needed to be written. Yet, they were not the only programmers who could do the work! With multiple programmers able to do the same tasks, vacations were possible and didn't threaten deadlines. It was even possible for these programmers to be assigned other tasks outside of the unit and broaden their own skills in statistical production work.