Data science
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Data scientists are in high demand due to their rare combination of skills in scientific background, computational, and analytical abilities, making them difficult to hire and retain. Companies recognize the necessity of leveraging data scientists not just for reporting, but for innovation and developing real-time solutions, as they thrive in environments that allow for exploration and creativity. With the increasing complexity and volume of data, businesses must strategically hire and empower data scientists to unlock the full potential of big data.
Data science
- 3. • If “sexy” means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. • There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills. So, what do they mean by the sentence “Sexiest job of the 21st century”
- 4. International Data Corporation (IDC) predicts a need by 2018 for 181,000 people with deep analytical skills, and a requirement five times that number for jobs with the need for data management and interpretation skills.
- 5. So, What is the typical work of a Data Scientist? What Data scientists do, is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible.
- 6. INSIGHTS: “Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil”
- 7. 1. Rigorous and copious efforts are needed to bunch the appropriated people and guide them through the market, where, there is a wild race going on. INSIGHT #1
- 8. • The companies must keep the image of the scientist in mind—because the word “data” might easily send a search for talent down the wrong path. • Some of the best and brightest data scientists are PhDs in esoteric fields like ecology and systems biology. • George Roumeliotis, the head of a data science team at Intuit in Silicon Valley, holds a doctorate in astrophysics.
- 9. Would be wise to wait until that second generation of data scientists emerges, and the candidates are more numerous, less expensive, and easier to vet and assimilate in a business setting?
- 10. If companies sit out this trend’s early days for lack of talent, they risk falling behind as competitors and channel partners gain nearly unassailable advantages. Think of big data as an epic wave gathering now, starting to crest. If you want to catch it, you need people who can surf.
- 11. 2. Data scientists want to build things, not just give advice. Data scientists want to be in the thick of a developing situation, with real-time awareness of the evolving set of choices it presents. INSIGHT# 2
- 12. Data scientists don’t do well on a short leash. As the story of Jonathan Goldman illustrates, “Their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer- facing products and processes.”
- 13. The dominant trait among data scientists is an intense curiosity— a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested. Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.
- 14. Employing the Insights into the life of a Manager.
- 15. Deductions from Insight #1 The sudden appearance of the Data Scientists on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. A manager must understand the following statement!! If the organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.
- 16. The company needs to be very choosy and strict while hiring a Data Scientist. Hiring a Quantitative Analyst, or a Data Management Expert would not fulfil the purpose. A quantitative analyst can be great at analyzing data but not at subduing a mass of unstructured data and getting it into a form in which it can be analyzed. Similar, A data management expert might be great at generating and organizing data in structured form but not at turning unstructured data into structured data—and also not at actually analyzing the data.
- 17. DEDUCTIONS FROM INSIGH A Data Scientist should by employed for the work he is entitled for, not just for reporting the work and making the presentations. If a company could direct the brain power and the abilities of a Data Scientist in the right direction, it could work out wonders for the company.
- 18. Examples of the companies, innovating with Data Science • LinkedIn isn’t the only company to use data scientists to generate ideas for products, features, and value-adding services. • At Intuit data scientists are asked to develop insights for small-business customers and consumers and report to a new senior vice president of big data, social design, and marketing. GE is already using data science to optimize the service contracts and maintenance intervals for industrial products.
- 20. Google, of course, uses data scientists to refine its core search and ad-serving algorithms. Zynga uses data scientists to optimize the game experience for both long-term engagement and revenue. Netflix created the well- known Netflix Prize, given to the data science team that developed the best way to improve the company’s movie recommendation system. The test-preparation firm Kaplan uses its data scientists to uncover effective learning strategies.
- 21. • Any Data Driven company should be very enthusiastic in hiring the appropriate people for the Data Scientist role. • Proper Training needs to be provided in the Data Science field, for which efforts need to be taken by the universities as well as the corporate industries. • Employing the Data Scientist in the work, he/she is entitled for, is the major thing to be kept in mind.