Source | www.datasciencecentral.com | Bob Hayes
Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates: Neural Networks – GAN (7%); NN – RNNs (15%) and NN – CNNs (26%).
A majority of enterprises (80%) have some form of artificial intelligence (machine learning, deep learning) in production today. Additionally, about a third of enterprises are planning on expanding their AI efforts over the next 36 months. But who will lead these data science projects? Who will do the work? Some researchers suggest there is a lack of AI talent needed to fill those roles. Tencent estimates there are only 300,000 AI researchers and practitioners worldwide. ElementAI estimates there are 22,000 PhD-level researchers working in AI.
Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning).