Data Science Trends To Look Out For In 2017
Data Science Trends To Look Out For In 2017Machine Learning is here to stay, with more firms following Google and Facebook in the race to attract the best machine learning experts and Data Scientists. We also see a merger of IoT and Data Science. Read on for more trends.
By Andrew Dipper.
The world of data science, big data and IoT (Internet of Things) is continuing to grow and adapt at an astronomical rate. Businesses are slowly able to piece together more information from different sources, meaning that they are able to make more sense of their data. Using data has become more and more important in creating new business opportunities and growth. Companies are still discovering the potential of utilising data and the importance of monetising that data in some form to benefit the business. Here’s what we can expect to see from the data science industry in 2017 – and how it might affect you.
Changes in data science qualifications
It’s been described as “summoning the demon” by Tesla’s Elon Musk, but machine learning is here to stay. Amazon, Facebook and Google have all entered the artificial intelligence race in recent years and in 2017 more businesses will look to attract the best machine learning data scientists to strength their departments.
But competition for jobs could get a lot tougher too. Don’t be surprised to see machine learning become mandatory for a career in data science from 2017 as more universities incorporate AI into their curriculums. If you want to stay ahead of the curve, there are a number of AI and machine learning certifications at your disposal. While these come at a high price – normally at least $10,000, give or take –there are numerous training courses on Coursera or edX that are free or at a low cost.
In terms of the other skills you need to succeed in data science, strong technology and coding knowledge, particularly using R or Python – but experience with SAS and MATLAB are also beneficial.
You also need to be comfortable working with relational databases, so SQL is incredibly important. In 2015 SQL was listed as the most important skill to have from a study of 3500 LinkedIn job listings. Hadoop, Python and Java were also prevalent.
IoT and data science merges
Despite a few key differences, data science and IoT are often seen as two sides to the same coin. In 2017 the two industries will come even closer together, with data scientists looking to access data from devices in real-time and to perform advanced analysis – or be used to make a decision.
So how does that work out in the real world? Think about it like this. In the not too distant future, you won’t need keys to enter your home. As you approach the front door, it will sense your presence and automatically unlock itself for you. Then, as soon as you leave the house, it will ask all of the non-essential energy units in the house to turn off – in turn saving the homeowner money.
This may sound like something you’d see on the Starship Enterprise but we should start seeing these relationships really take shape in 2017 – and you need to make sure you have the skills to jump on these projects.
As well as AI, data science for IoT means you should be able to work with RIL (radio interface layer) across a variety of devices, edge processing, real-time processing and deep learning.
The rise of big data technologies
We’ve already seen this grow astronomically in 2016, but in the next year budgets for big data technologies will rapidly increase as it becomes more widely accepted amongst businesses. Most companies have already identified that they need to improve this area of business, which will, in turn, lead to more data scientists being needed to handle the masses of extra data they have to access.
If you’re looking to forge a career in data science, knowledge of big data and data frameworks are essential. You want to specifically look at Apache Hadoop, HDFS, Hbase, Spark, Storm, Solr, and Kafka.
A healthcare industry led by data science
Data science has already been invaluable in improving the outcomes of epidemics and predicting patient behaviour. In 2015, data scientists helped predict further West Nile virus outbreaks in the United States, with 85% accuracy. And earlier this year, a team of scientists developed a model that can predict the likelihood of bats carrying Ebola. Expect data science usage in the healthcare industry to grow further in 2017 as healthcare professionals look for ways to improve day-to-day needs and save lives.
With the rise of electronic healthcare records the amount of data at our disposal is at an all-time high. While massive amounts of data has its benefits and drawbacks, there are many lucrative opportunities for scientists looking to decipher this data in 2017. If you’re looking for an emerging market to work in, this is it.
Bio: Andrew Dipper is a freelance journalist writing about technology, business and marketing.
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