Data science, big data, and the Internet of Things are evolving and evolving at a dazzling rate, and the business community is slowly integrating more data from different sources and gaining more insight into it. This article is Andrew Dipper's outlook for the data sciences industry in 2017 and lists four important trends.
The rise of big data technology
In the past year, we have witnessed the phenomenal growth of big data technology, but as big data technology is widely accepted in the corporate world, the budget left for big data technology will increase rapidly in the next year. Most companies have identified the need to improve the data direction of their business, which in turn will require more data scientists to handle the large amount of additional data that companies need to retrieve.
If you are pursuing a career in data science, knowledge about big data and data frameworks is a must. You can pay special attention to the following platforms: Apache Hadoop, HDFS, Hbase, Spark, Storm, Solr and Kafka.
Changes in data science qualifications
Although Elon Musk believes that machine learning is "summoning demons," it will continue to evolve. Amazon, Facebook and Google have all joined the artificial intelligence competition in recent years. In 2017, more and more companies will recruit top-notch machine learning data scientists.
But at the same time, the competition for jobs has become more intense. In the context of more and more universities including AI in the curriculum requirements, machine learning will become a prerequisite for entry into the data science industry starting in 2017. If you want to get a head start in the competition, there are quite a few AI and machine learning certification courses for you to choose from. Although some courses cost $10,000, there are also a number of free or low-cost training courses on Coursera or edX.
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