Careers in data science are soaring. By 2020, the number of positions for data and analytics talent in the United States will increase by 364,000 openings, to a total of 2,720,000.
But what percentage of those roles will be filled by women? According to research from executive recruiting firm Burtch Works, 85 percent of Data Scientists and 74 percent of Predictive Analytics professionals are male. By either metric, less than thirty percent of roles are filled by women.
The growth of the Analytics sector is running up against an increasing shortage of talent in all roles, from technical to managerial. Women constitute 50 percent of the population and should not be excluded from these career opportunities.
While data doesn’t lie, the results generated by analytics processes are dependent upon what queries are made in the first place. Women may define problems and possibilities in quite different ways than men. Companies that don’t encourage diversity in their data ranks could miss out on discovering new insights that could lead to new business opportunities. More diversity equals smarter, more efficient business.
This initiative helps companies set a better habit to generally consider women for data-intense roles.
We are elevating 24 unique women, currently practicing data science and machine learning. The series will run for 12 months, featuring bi-weekly syndicated content comprised of: blog posts, a podcast and a short video.
Application failure prediction with anomaly detection by Mythili Krishnan
Server downtime is a cause that plagues the applications that it supports. Continuous availability of servers and its related applications is critical, and a negation can cost a lot for companies in terms of dollar as well as productive time. Early alert generation method would help detect when the servers might fail or cause critical incidents and take anticipatory actions that could help avoid any losses to the business. Mythili Krishnan, a Senior Analytics Leader, will talk about exploration of the detection methods that can be utilized to predict when an application will fail with the help of a ecommerce use case along with a short demo in python.