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Recap of Data Science News for November 2017

Data Science News - November 2017

Data Science News for November 2017

 

How Cloud Will Elevate Data Science Teams. Forbes.com, November 1, 2017.

With increasing amount of data generated by sensor, devices and users, it is becoming insurmountable for data scientists to curate, organize and process information. As data-driven businesses continue to evolve, cloud is becoming the common denominator that helps data science teams with the right tools to glean intelligent insights and share it across organizations. Cloud is becoming the foundation of data potential making it a critical component not just for the success of the data science teams but also for the entire organization. Whether it is data governance or giving life to unstructured data or embracing various data languages and tools on the rise – cloud is powering the way data science teams make impactful business decisions.

(Source:  https://www.forbes.com/sites/forbestechcouncil/2017/11/01/how-cloud-will-elevate-data-science-teams/#3913fde97185 )

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Data science for all not just a pipe dream. SiliconAngle.com, November 1, 2017.

A solid data strategy has become the heart of all business functions, however, it comes with several challenges. According to Daniel Hernandez, vice president of offering management at IBM Analytics, major issues with data quality lie in data integration. Daniel further mentions that the major reason for poorly functioning data processes is the knowledge difference between data governance and data science teams. Any client purporting a data science problem often has a data management issue around data discovery. To do data science, it is necessary work through various data discovery problems with clients.
(Source:  https://siliconangle.com/blog/2017/11/01/data-science-not-just-pipe-dream-dsforall/)

The great data science hope: Machine learning can cure your terrible data hygiene. Zdnet.com, November 12, 2017.

Without clean data , a data scientist cannot create  machine learning algorithms or model for  analysis. Data Lakes were considered the magic box for data cleansing but they did not quite pan out well when it comes to improving data hygiene and now it;s machine learning to save the day. With human power not being precise enough in cleaning data, machine learning can clean the data on the fly. Machine learning might not be able to fix the decades of poor data hygiene but definitely comes to the rescue in saving the data management hide.
(Source : http://www.zdnet.com/article/the-great-data-science-hope-machine-learning-can-cure-your-terrible-data-hygiene/ )

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Obama’s former chief data scientist, DJ Patil, joins Venrock as an adviser. TechCrunch.com , November 14, 2017.

DJ Patil, the person behind solving some of the biggest nation’s problems in areas of policing, crime and medicine has joined Venrock Capital as an adviser. Venrock Capital has invested in top organizations like Nest, Cloudflare, Illumina and Dollar Shave Club. DJ Patil, the chief data scientist under Barrack Obama will work with the investment team at Venrock and various portfolio organizations under security, healthcare and other areas.“My primary focus is building new models to improve healthcare and Venrock is the best out there,” Patil told TechCrunch in an email.

(Source : https://techcrunch.com/2017/11/14/obamas-former-chief-data-scientist-dj-patil-joins-venrock-as-an-adviser/ )

Karnataka to set up centre for data science, AI. NewsPatrolling.com, November 16, 2017.

The government of Karnataka together with Nasscom has announced the launch of Centre of Excellence for Data Science and AI to boost the state’s prowess of technology. The CoE will provide infrastructure, technology , promote investments into research and foster the innovation of data science and AI solutions making India a destination for global products and solutions on data science and AI. The centre will solve the most complex problems across the globe with data backed solutions. This CoE will be first of its kind that will be set up at a cost of  Rs 40 crore on a public private partnership.

(Source : http://www.newspatrolling.com/karnataka-to-set-up-centre-for-data-science-ai/ )

Four Keys To Building Your Data Science Function From DJ Patil, DataScience.com's New Board Advisor. Forbes.com, November 20,2017.

DJ Patil joined the Advisory Board of DataScience .com, an enterprise platform that fosters scalable collaboration between businesses, IT and data science teams. In a discussion with William Merchan, Chief Strategy Officer of DataScience .com , DJ Patil highlights four key themes that every leader should when developing a sustainable data science program-

  • The key to success is how well an organization can integrate their engineering and data science teams.
  • The company should focus on building the data science function instead of focussing on the shortage of data scientists.
  • Make use of interactive tools to integrate data science teams with non-technical teams.
  • Don't believe the hype around the fact that data scientist are going to automate themselves out of a job, humans will be necessary for a long time.

(Source : https://www.forbes.com/sites/everettharper/2017/11/20/four-keys-to-building-your-data-science-function-from-dj-patil-datascience-coms-new-board-advisor/#58f83b436644 )

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10 Questions Executives Should Be Asking Before Hiring A Data Scientist. Forbes.com, November 20, 2017

Hiring a data scientist has always been considered a difficult and time-consuming task because of the ever-growing demand and skills gap among the applicants. Piero Ferrante, Senior Director & Principal Data Scientist at C2FO highlights 10 important questions that every executive should ask before they move on to hiring a data scientist for their organization -

  1. Does your business really need a data scientist ?
  2. Do you need to hire a data scientist or contract ?
  3. What attributes you should look for when hiring data scientists ?
  4. Does your business have enough data ?
  5. Does your organization know what you are measuring ? (Define the problem and goal beforehand)
  6. Does your organization have a data team ?
  7. Do you need to hire a specialist or a generalist ?
  8. What are skills you need to look for when hiring data scientists for your business?
  9. Do you need to hire a candidate with a MS or Ph.D ?
  10. Is your organization committed to being data-driven ?

To  unlock the answers to the above questions, read the complete article on Forbes.

(Source : https://www.forbes.com/sites/forbestechcouncil/2017/11/20/10-questions-executives-should-be-asking-before-hiring-a-data-scientist/4/#79f2ca8e7f9d )

Hiring vs. training data scientists: The case for each approach. TechTarget.com , November 21, 2017.

Organizations are often in a dilemma whether to hire a data scientist or train their existing employees to cater to the increasing demand of implementing data science. Each case has its own approach and depends on the organizational requirements. Considering the shortage of data scientists, interviewing and hiring quality data scientists is definitely resource and time-intensive. However, training the existing employees can turn out to be futile if the employees do not have the required basic aptitude skills. If you are hiring a data scientists, he/she brings in novel ideas and capabilities into your organization.To the contrary training your existing employees in data science will add to your existing expertise . For any organization weighing the options whether to hire or to train data scientists is completely based on specific business needs and finding the right balance is important.

( Source : http://searchbusinessanalytics.techtarget.com/tip/Hiring-vs-training-data-scientists-The-case-for-each-approach )

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