Data science occupations are amongst the top coveted careers in the US, currently taking the leading spot, particularly on Glassdoor’s list of the Best Jobs in America for the last three years.
They also boast high average salaries, particularly for those with a suitable skill set. According to a recent report from Figure Eight revealed that professionals in this line work reported a high job satisfaction. Also, 89% of data scientists confirmed that they love their occupation, which is an increment from 65% recorded in 2015.
The demand for data scientists is still high. In fact, the report found out that 49% of all the 240 data scientists involved in the survey reported that they are contacted for a new job at least one time per week.
The reason for this is that additional companies are growing not only their collection but also their use of data. Furthermore, they require an expert who can leverage it to push business insights and incorporate it to new technologies such as machine learning and artificial intelligence.
The report found out that about 90% of data scientists are convinced that some of their functions inform machine learning and artificial intelligence (AI) projects. Almost 40% reported that most of their work is capable of doing so.
Nonetheless, the work comes with its fair share of challenges, whereby almost 55% of respondents claimed that the quantity and quality of training data is their main challenge.
Both machine learning and data science remain in young fields. With that, there is a lack of sufficient consensus on what frameworks, tools, and languages are ideal.
Although the report revealed that the machine learning community largely started utilizing Python, there is still broad variability, specifically in the machine learning frameworks that are presently in use.
According to the report, here are 10 leading machine learning frameworks that data scientists use:
- Google Cloud ML Engine
- Pytorch & Torch
- AWS Deep Learning AMI