Even professional engineers are often dismayed by the pace of change in the technology industry. One of the biggest examples is with the emergence of machine learning. Only a few years ago, very few people had ever heard of the term. Today, it is growing faster than ever. One study shows that the machine learning market will be worth over $19 billion by 2023.
Since demand for machine learning solutions is growing at an incredible pace, developers with experience in programming languages that lay the foundation for machine learning technology will grow as well. Python is one of the most popular machine learning programming languages.
Machine learning appears to be driving demand for Python developers
Coding Dojo published an article on the employment landscape for programmers. There were two pieces of good news for Python developers:
- Python is the second most needed programming language. It is ranked right after Java.
- Demand for Python Jobs is growing at a faster rate than almost any other programming language. The number of Python job openings increased from 41,000 to 46,000 between 2017 and 2018. Although Java is still a more popular programming language, demand actually shrunk in that timeframe.
There are various reasons that demand for Python programmers might be increasing. However, as Coding Dojo suggests, the biggest reason is probably that machine learning is driving the need for new Python programmers.
In what fields are Python programmers with a background in machine learning most in demand?
A number of fields are leaning more heavily on machine learning these days. Employers in these industries are highly likely to hire Python developers. Python programmers with an interest in machine learning should be aware of the opportunities in different industries and the types of projects they would likely work on. Industries with a high software development budget can afford to offer exceptional salaries.
Here is an overview of Python programming needs in different verticals.
Digital security
Cybersecurity concerns are mounting in almost every industry. According to a 2018 report by Cisco, 31% of all organizations have experienced at least one data breach. That figure is expected to keep rising for the foreseeable future.
Organizations that want to mitigate the risks of cyber attacks need the most up-to-date technology. The cybersecurity industry is constantly rolling out new cyber defences, which rely more heavily on machine learning. As Alexander Polyakov of Towards Data Science points out, machine learning could be the only solution to thwart hackers, since they seem to always be one step ahead of cybersecurity experts.
In the era of extremely large amount of data and cybersecurity talent shortage, ML seems to be an only solution, Polyakov writes.
Most machine learning code for cybersecurity is written in Python.
Financial industry
The financial industry is also Investing in machine learning technology. The number of applications in this field is virtually limitless. They include the following:
- Developing a clearer understanding of the risk profiles of borrowers and insurance beneficiaries
- Making more informed, real-time financial trading decisions
- Identifying previously overlooked merger opportunities
- Improving valuation models by accounting for hundreds of variables that previously could not be factored into consideration by human analysts
The financial industry is going to need even more Python developers in the future as machine learning becomes more essential to its core business model.
Social sciences
When you think of fields that depend on machine learning, the social sciences field is not the first that comes to mind. However, there are a number of machine learning applications in the social sciences and humanities.
Social science professionals need to factor for their own bias. Machine learning can help them identify correlations between variables that they would have previously overlooked in their research.
