HR analytics is not just for technical experts or HR practitioners, but really for all individuals in an organization including employees (both HR and non-HR jobs), managers, leaders, and entrepreneurs. By definition, HR analytics is the data science of management and informs various HR-related decision-making processes with evidence obtained from existing data of people in an organization. For HR practitioners, HR analytics provide the tools, knowledge and evidence for organizations to make better investments in people and become more successful in accomplishing organizational goals. For non-HR employees and line managers, HR analytics provide knowledge of how to comprehend the data analytic results and how to utilize such insights to make better decisions for future management practices. Top leaders or business owners can also rely on evidence from HR analytics to (1) ask the relevant questions or issues with existing management practices, (2) understand the results of HR-related investment at the aggregate level, and (3) make more informed decisions related to their existing employees and new hires in the future.

While greater Excel and statistical skills would surely help, students even with a basic level of such skills can successfully get to work on our analytics case over a semester (or two). To facilitate this learning experience, our HR analytics workbook is designed to start with exercises with basic Excel skills from the first module. This Excel and related statistical knowledge gets gradually sophisticated throughout the workbook. Over the semester, students' fear of Excel generally alleviates as long as they consistently work on the exercises.

By the end of the semester, what students usually struggle the most is not necessarily the Excel/statistics skills, but the lack of training in building logical and convincing storytelling of evidence found from the analyses (not the analyses per se). Again, HR analytics is not only about analyzing the data, but essentially about bringing good logic to inform managerial decision-making with the findings.

Technically, Artificial Intelligence (AI) is about trying to make machines think (or pretend to think). Machine Learning (ML) and Deep Learning (DL) are the two large subsets of AI. ML is about how computers can use the data to come up with better decisions. Within ML, we find 'supervised learning' that copes with labeled data; 'unsupervised learning' on Clustering unlabeled data; and 'semi-supervised learning' with some labeled and unlabeled data). ML often involves smaller samples of data and thus requires human intervention in data analysis process. DL, however, is an automated process with no need of human intervention during its analytic and decision-making processes. To do so, DL requires extremely large samples of data, or so-called "Big Data".

To our understanding, HR analytics can be considered as the 'supervised learning', or the subset of ML. In organizations HR or any related managerial data are just too small to be considered as DL. Moreover, human should intervene the analytic processes to deliver evidence-based insights and inform the decision-making processes.

Yes, the workbook has been designed to complete over a given semester. Focusing on the workbook modules with no other exam or test-based assessment, we encourage students to work regularly and consistently over the entire semester as if this is for their job or living. Students performance usually do follow their learning curve and become much faster and efficient to work on the later modules of the workbook toward the end of the semester.

While students can utilize other statistical software (e.g., SPSS, JMP or R) to work on the workbook exercises, we recommend students to accomplish the course by using just Excel. Several reasons for this: First, Excel is the most popular and available (and free!) software in most companies today. Companies do not necessarily spend extra budgets just to purchase other paid software or application

We designed the workbook and the Mr. Macky's case only as an Excel version (and teach HR analytics solely using Excel) for students to fully understand the entire analytic processes. Recommending Excel as the primary tool for instructors and students, we further suggest students to build more analytic skills by working on the same workbook yet using other sophisticated statistical sofrware such as R (and R is free too!).

High utility of our case and workbook for students were among the top goals when we had developed these materials. As each exercise in our HR analytics workbook can easily become a real-world analytic project in any organization, the utility of the Mr. Macky's case has been historically high for students at work after their graduation. For instance, some have become HR middle managers practicing 'utility analysis' (i.e., HR version of cost-benefit analysis) from the case workbook and refer to the findings when making HR intervention decisions (e.g., training or reward programs) for their employees.

Moreover, we hope our materials could enhance individual students' skills and future career opportunities. Indeed, some of our students in the past briefly presented their works during their job interviews and make themselves more probable candidates during the hiring process. Mr. Macky's case, therefore, conveys a great deal of potential for students (and faculty who advise them) to accomplish various short- and long-term career goals.

Organizations vary in terms of 'data literacy' or the extent to which key managerial decisions are made based on analytical evidence. Even if your current or future job/organization does not practice any evidence-based or data-driven approach, or even if there have been no dataset exists, HR analytics is a very doable practice to start. Even the organizations with no data would in fact have good data in their key HR areas such as performance ratings, compensations, turnover, demographics of employees, selection device score, etc. As our HR analytics workbook contain a wide variety of exercises within and across HR areas, students may also refer to specific exercises from Mr. Macky's to easily adapt and call it a HR Analytics project in their organizations. It may not be an easy thing to fight for, but something that can surely bring unexpected contributions to business success.