The world biggest trends in 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 utilizing data and the importance of monetizing 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.
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.
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 some key differences, data science and IOT are often seen as two sides of the same coin. By 2017 the two industries will come even closer, with data scientists trying to access device data in real time and perform advanced analysis or be used to make a decision.
2016 may have just begun, but predictions are already started for next year. Data science is rooted in automatic learning, and many expect this to be the year of Deep Learning. With access to large amounts of data, deep learning will be key to moving towards new areas. This will go hand in hand with opening data and creating open source data solutions that enable non-experts to participate in the data science revolution.
In the last decade, the idea of data science exploded and little by little became what we recognize today. A vital point that analysts understand is that data science and large data are not simply “magnification” of the data. Instead, it means a change in study and analysis. Although it seemed almost completely ordinary in today’s world, as something that could not be removed from research and study, the nature and importance of data science was not always so clear, and its exact nature will continue Developing along with technology.
The data science of learning forces you to learn useful auxiliary skills. The construction of the technical attributes to apply yourself is the top line. There is something unique about being able to predict scenarios or identify trends in amorphous bits of data. But one must have the right combination of data visualization, analysis, predictive modeling, supreme organization and communication.
Another valuable skill is coding (Python and R, as a minimum) to further strengthen your data science skills. The coding is valuable and extends your future options; It also makes you think logically and in terms of algorithms. In addition, data scientists are adept at learning to handle unusable or irrelevant data.
The other obvious skills that need to be learned are probability, statistics, and deep contextual understanding of any given situation, all of which are incredibly useful for prediction under uncertainty.
Lastly, and perhaps most importantly, learning the science of data requires you to learn how to communicate and how to tell a compelling story based on the data you see.
So how does data science maximize the choices of your future? As a field that is gaining importance in all industries, data science is not going anywhere soon. Data-savvy scientists are professionals with training and curiosity to make discoveries and informed, quantitative decisions in the world of great data.
As a field, data science rose to prominence due to record levels of raw material: structured and unstructured data. A couple of secular forces resulted in this race, including lowering the cost of data storage and increasing accessibility through cloud networks. The number of sensors is increasing (satellites, cameras, telephones, internet things and so on). Computational power is increasing. Information extraction algorithms (eg, machine-learning techniques) are becoming more sophisticated. All these forces go in one direction, which makes it very likely that the ability to dispute data to produce a useful vision will be rewarded over the next few decades. Recognizing this situation, Wharton recently announced the introduction of data science in its MBA curriculum.
Employers are hiring data scientists in every industry. In the competitive labor landscape, which sees an endless stream of information and communication, data scientists help key decision makers move from an ad hoc analysis to a continuous conversation with the data. Thus, thousands of data professionals have made their mark in both new and well-established companies. In making their findings, they communicate what they learn and make suggestions for possible business directions and ongoing organizational processes. Its sudden appearance, mainly in business environments, is a testament to the fact that companies are struggling with diverse sources of information in volumes never before found.
Whether you like financial markets or bio pharmaceuticals, robotics or the media, data scientists are present everywhere, even the United States government recently named a chief scientific data. Networks connect data scientists with nonprofits doing an astonishing and significant job, and these positions are in demand in both established and new businesses. It is not necessary to choose an industry or domain; You can keep your options open with data science.
Last but not least, a career in information science makes you make better decisions in other areas of life. At its core, data science education makes it a data driven decision maker. More than anything, what data scientists do is make informed discoveries when they are surrounded by copious amounts of information from several sometimes conflicting sources. Then structure these data and do an educated analysis.
When it comes to examining information, automation and other technological processes – such as automated learning, algorithms and analytical systems – are essential elements that will enable you to do your job effectively. Instead of relying on his “expert” instinct or judgment, he develops the habit of seeking data to refute or validate all his hypotheses. You can begin to formulate these hypotheses in a business context.
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