While engineering remains a critical asset, the rise in cloud and XaaS services has affected computer and hardware roles such as server administrators, computer hardware support technicians, and professionals who work on the hardware side of router and storage management.2 The COVID-19 pandemic has hit electrical and hardware design engineering roles harder than others in the tech industry.3 By contrast, even as the pandemic was worsening business conditions in spring 2020, tech majors' job openings for data analyst, data engineer, and data architect roles continued to trend high.4 Tech companies have long been at the forefront of attracting professionals with advanced analytical skills,5 and since 2014, tech recruiters have particularly targeted professionals with math and statistical skills, looking to harness their ability to study and analyze data to help solve real-world business issues.6 The race to AI has accelerated the crunch, as the top Silicon Valley companies have ramped up their workforce aggressively, focusing on advanced analytical skills such as ML, natural language processing, data engineering, and data visualization and image processing.7 Demand for data scientists and ML and AI specialists began surging in 2016.8 Tech companies continue to ramp up data scientist and data analyst talent.9

Suppose you are the hiring manager for a data scientist position, and interviewing a prospective candidate. The candidate starts to express the skills hoping they are enough for the position and the best card among these skills is MS Excel capability. What would you think about this candidate? I suppose most of you would consider this candidate as mediocre, which is ineligible for most of the companies. Let's make a little change in our hypothetical interview by replacing MS Excel with predictive modelling.

Shell used AI to monitor and predict demand for EV charging terminals on its forecourts. Professionals will need to learn data science skills to do their jobs and help their companies thrive in the next decade, say business leaders. Most managers believe data analytics, automation, and AI will be essential for business survival in the coming years yet lack the necessary knowledge that underpins it, according to MHR Analytics research. "We wanted to explore the levels to which organizations across all sectors are developing their data strategies, as businesses get ready to enter a new decade that promises unprecedented digital acceleration," said Laura Timms, MHR Analytics Product Strategy Manager. "Without the crucial component of a good data foundation, it is impossible to implement advanced analytics, automation or AI," she said.

This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases.

This column was originally published on Jeff Neal's blog, ChiefHRO.com, The government is sitting on a treasure trove of HR data that it does not typically use. For example, agencies have data about performance, and data about where they recruit and what kinds of questions they ask in job announcements. I do not know of a single agency that is comparing the questions they ask to the performance they get from the selectees. There are so many possibilities to use the data to produce actionable information that would help agencies do better hiring, get better performance, and use their resources more wisely.