The tech skills gap is exacerbated by the fact that some of the toughest roles to hire are in data and analytics.
Digital Nation Australia spoke with Anna Russell, data scientist and director of Polynomial Solutions. She said the most in-demand technical skills in data science are niche skills around machine learning and coding languages.
“In particular, the more advanced areas such as deep learning image recognition and image processing are in high demand,” Russell said.
From a linguistic point of view, there is a strong demand for analysts who can code correctly. Obviously, Sequel is still very popular because many companies use Sequel as their primary database language and Python and R are the two popular languages for analytics.
While some of these skills such as coding using Python are in demand due to great need, others, such as more advanced fields, are niche skills where there is a serious need. lack of supply.
Depending on the industry, the type of data and analytics skills required varies.
According to Russell, technical skills such as data processing and ease of using data visualization tools like Tableau are in demand when it comes to campaign reports, operational reports and product reports.
“It really lends itself to a solid understanding of visualization, which can be useful for the business,” she said.
However, in a sector like healthcare or pharmaceuticals, Russell emphasizes the importance of traditional statistical skills.
“You may be looking for someone who needs a strong background in statistics, with less emphasis on data and feedback because you have a high level of quantitative literacy in general.”
Embedding a data-driven decision-making culture depends on having a quantitatively-driven leader, she said. Someone who is “willing to spend the money and put the effort” into the infrastructure supporting data science.