Machine Learning is gaining mainstream presence due to the big data explosion and its varied frameworks and analysis tools. In the coming year machine learning is expected to change in the following ways – techies will continue to remake themselves with machine learning as they did with cloud computing, apache spark will gain momentum, machine learning itself will be open source and the struggle for data sources to feed machine learning will get more heated.
At a recent European seminar on ‘The Input of BioMolecular Biobanking to Data Analytics and Medical Research.' – both the Input of BioMolecular Biobanking to Data Analytics and Medical Research are working to bring together biological samples that is used for medical research and the patient data that is stored along with the samples – to use data analysis for ground breaking discovery for healthcare.
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Riley Newman, head of Data Science at AirBnB, has recently published an articles detailing how AirBnB uses data science to – analyze customer feedback efficiently. They capture the customer feedback and use data science to effective capture the voice of the customer. AirBnB are now looking to further enhance their data science team by hiring qualifies talent.
Every company’s data science needs will vary. This is why it is so important to build the right kind of data science teams in order to create profitable big data projects. Miles Johnson and Sam Hochgraf of IBB Consulting Group, list out a detailed step by step process on how to hire the best Data Science team in any company.
DHL – one of the largest logistics company just unraveled it research on how supply chain management data can augment productivity. The supply chain data is mainly untapped, but it is has a treasure of information which can improve the business processes if correctly analysed with the help of data science.
Where a decade ago it would have been really difficult explaining the role of a Data Scientist to someone, now, it is difficult to stop companies from hiring data scientists. It all made quite a shift in the last 5 years when big data analytics took over the market. There is an anemic growth in supply and a global shortage of talented Data Scientists. The global talent dearth is mainly because Universities do not yet provide Data Science as an elective.
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The tech giant Oracle is expecting Big Data deployments to become more mainstream in 2016. This has led them to make a series of prediction for the coming year, some of them being – the demand for data scientists is going to increase, data virtualization will become a reality, data civilians will start to behave more and more like data scientists, big data cloud services will help with IoT.
Data Science is slowly starting to augment all departments from sales to content and for this reason all publishing houses are combing through applicants for seasoned data scientists who know the media industry. But this is easier said than done as it is extremely difficult to find a qualified data scientist who has a background the in the media industry.
Booz Allen Hamilton’s new platform called Sailfish aims to help companies who are not yet able to hire qualified data scientists. Sailfish is designed to make data analytics easier for users. Within Sailfish there is a Sailfish Exchange – which has a highly usable data library, where users can upload and share key data points.
In order to widen the scale, impact and scope of computer science research into Data Science, the University of Chicago has brought in Data Science specialist and scholar Michael Franklin to serve as a senior advisor in the Computer Science department.
Australian based software and artificial intelligence company – Complexica had reaffirmed the beliefs that data science can help in the growth of a company. It has recently hired Nigel Hey, data chief for Treasury Wine Estates to lead its data science team.
The past semester at MIT’s Sloan School of Management, Analytics Labs (A-Lab), saw a host of companies submitting projects – their business problems to be solved through data analysis. The objective of this course and experiment at A-Lab, was to find out if Data Science can be used to solve real life business problems. Data Science is still a maturing discipline and solving business problems requires quite a bit of domain expertise. It will be interesting to see how data science develops in today’s market.
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