A recent survey reveals the stark contrast between the adoption and proficiency rates of machine learning in data science. According to the findings, a whopping 75% of data scientists use machine learning in their work, but only 25% possess the necessary skills to effectively apply this technology.
The survey also highlighted the significant gap between the perceived importance of machine learning and the actual proficiency levels. While 95% of respondents believed that machine learning was crucial for their organization’s success, only 35% reported having a strong understanding of the subject matter.
Furthermore, the study found that data scientists with a background in computer science or mathematics were more likely to be proficient in machine learning (45%) compared to those with a non-technical background (15%). The survey also revealed that the majority of respondents (60%) believed that machine learning was becoming increasingly important for their organization’s competitiveness.
These statistics underscore the pressing need for data scientists to upskill and reskill in order to remain competitive in today’s rapidly evolving data landscape.
Data Engineering Community (DEC)
@data_dec
·
Jul 3 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐯𝐬. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭:
◉ Focuses on extracting insights from data using statistical and machine learning techniques.
◉ Proficient in programming, data analysis, and visualisation.
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