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As we’ve explored in the past, the need for data professionals is soaring and trending only upward, with an expected 28 percent rise in demand over the next two years and a projected 2.7 million new jobs. Roles for Data Scientists have increased by 650 percent since 2012, and few expect that trend to reverse.
Meanwhile, those in the industry are seeing a larger and larger focus on machine learning. BrainStation’s 2019 Digital Skills Survey found that machine learning was the trend most likely to have an impact on the work of data professionals, with 80 percent of data respondents singling it out as the top difference-maker in the coming five-to-10 years. And Google Trends shows that its popularity as a search term has grown roughly 200 percent over the last three years.
The question is, what’s the difference between data science and machine learning? We took a closer look.
To put it simply, machine learning – or the process of a computer learning how to better perform a task as it gains more experience doing so – uses algorithms to make predictions and find patterns. Machine learning spans a wide array of ideas, tools, and techniques used by Data Scientists and other professionals, and it’s one of the most popular methods for processing big amounts of raw data.
It might be easiest to view machine learning as a part of data science. Machine learning frees Data Scientists from the tedious task of sifting through massive volumes of data by using complex algorithms and problem-solving methods including supervised and unsupervised learning, regression, classification, clustering, and neural networks.
Examples of machine learning are all around you. Facebook, for instance, uses machine learning to analyze your past behavior to present content and notifications in line with your interests. Similarly, when Netflix somehow recommends a show you’d love to binge-watch, it’s an example of machine learning.
Perhaps the simplest example of machine learning in motion lies in how it approaches the task of recognizing handwriting. To train a machine with examples of correct input-output pairs – which is called supervised machine learning – the computer is shown images of handwritten numbers alongside the correct labels for those digits. The computer then tries to figure out the shared characteristics of each digit, and gradually picks up on the patterns between the images and the labels.
Generally, machine learning is effective to solve problems that are statistical or probabilistic in nature, deeply complex, and that can be adequately handled with an approximate solution. For instance, the issue of detecting credit card fraud checks those boxes: solutions are probabilistic because a determination won’t be made until a company reaches its customer; the rules around fraud are complex; and approximate solutions are adequate since we’re simply flagging transactions for further review.
Although many of the more advanced machine learning tools do require some experience and knowhow, the basics can still be impactful for those looking to dig deeper. Many supervised and unsupervised learning models are implemented in R and Python, and straightforward models like linear or logistic regression can be used to perform informative machine-learning tasks.
Data science uses technology and math to find otherwise invisible patterns in raw data to help us make predictions and better-informed decisions. As more and more data is generated and collected by companies, data science is the key to finding otherwise hidden insights that can have a transformative effect on factors including consumer behavior, operational issues, marketing opportunities, supply-chain cycles, and predictive analyses.
A Data Scientist spends significant time collecting, extracting, cleaning, modeling and analyzing data before using an array of techniques to come to meaningful conclusions, including predictive causal analytics (or predicting the possibilities of an event in the future), prescriptive analytics (conjuring a range of actions and the related outcomes) and, yes, machine learning.
It might sound as though Data Scientists spend their days buried in numbers, but that isn’t the case. BrainStation’s Digital Skills Survey found that Data Scientists primarily work on developing new ideas, products, and services, as opposed to optimizing existing platforms.
And while a majority of respondents (73 percent) indicated that they work with numerical data, 61 percent said they also work with text, 44 percent with structured data, 13 percent with images, and 12 percent with graphics. Some even work with video and audio.
In all cases, however, the goal of data science is to leverage data to help a company make better decisions. And given the sheer volume of data being generated and now analyzed, data science is a field that has never been more important.