Data Scientists-The Banneret’s of Data Science!
Ever wondered what flying cars and Data Scientists have in common; they are both elegant and beguiling and possessing a data scientist or two on staff is the very fashion statement in the world of Big Data and Analytics. Data Science falls into many roles and thus its significance in industry. The new technology isn’t just here to scream “Eureka’’ but the relevant motive is to have that triumphant cry by preparing a package of insight and deliver it for the company’s front line operations. But, who deduces this; Yeah! It’s none other than our technology ‘Paladins’, the Data Scientists.
Being able to accumulate data at an optimum scale is less than half of the battle of data science. The other half is more composite as managing the data science insights and inserting them adequately into business processes and tactical systems is a part of the meld. Data Scientist show up their knack and caliber in everyday decisions of company including the product mix changes, preventive healthcare communications , price changes, customer retention program initiatives , service level tier retentions and many more.
There is a lot buzz about not being enough of skilled Data Scientist in the world. While it is certainly true for the moment, we can overcome this problem by creating a data science ecosystem-the infrastructure, the people and the process and frame it for a business that is ululating for success. And as we get this in place, we would surely race our cool cars in frenzy!
So what does a data scientist do?
A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems; they will pick the right problems that have the most value to the organization.
- Makes sense out of any data and answer following: What is happening? What will happen? What we should do? What do we learn?
- Create a powerful visualization so business users can comprehend.
The data scientist role has been described as “part analyst, part artist.” Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.
Who needs Data Scientist?
Every entity which generates data, needs Data Scientist to makes sense out of data.
- Oil and gas
- Banking and Finance
Which companies are hiring?
Here are few firms which are hiring Data Scientists:
- Myntra Designs Pvt. Ltd.
- Fractal Analytics
- JPMorgan Chase & Co.
- Cognizent etc
We have scanned through over 100 Data Scientists jobs and found out following most common skills for the job:
- Domain Knowledge
- Strong understanding of Statistics, Machine Learning, NLP, Text Mining, IR, Pattern Recognition and Visualization
- Exposure to tools like: R and Python, Hadoop, Spark, Tableau, IBM Info sphere Big Insight, AWS
Where do I learn these skills?
- Short Courses: MOOC platforms like Coursera, Edx and mUniversity.mobi offer you a lot of free and paid short courses to acquire knowledge in these areas.
- Data Science Programs: MS in Data Science at NYU; PGP in Business Analytics and Big Data, a holistic Data Science program by Aegis School of Business & Telecommunication in association with IBM in India. Program is available in Full Time as well as Executive Part Time Online, Hybrid and On Campus models.
- Attend Meetups like http://www.meetup.com/Aegis-IBM-BIG-DATA
- Read Blogs