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Question 7 : Imagine you are trying to hire a Data Scientist for your team. In addition to technical ability and quantitative background, which additional essential trait would you look for in people applying for this position?

1.    Communication skill
2.    Scientific background
3.    Domain expertise
4.    Well Organized

Correct Answer : 1

Exp : let's discuss how you can be on your way to be an effective Data Scientist.
 
1.    Diverse Technologies - a good Data Scientist is handy with a collection of open-source tools - Hadoop, Java, Python, among others. Knowing when to use those tools, and how to code, are prerequisites. To be a Data Scientist, you should have your hands on a number of tools and technologies, especially open source ones, such as Hadoop, Java, Python, C++, ECL, etc. Besides, having good understanding of database technologies, such as NoSQL database like HBase, CouchDB, etc. is an add-on.
 
2.    Mathematics - The second skill, as you might expect, is a base in statistics, algorithms, machine learning, and mathematics. Conventional computer science degrees no longer satisfy the quest of a data scientist. The job requires someone who on the one hand understands large-scale machine learning algorithms and programming and on the other is a statistician. So, the profile is better suited for experts in other scientific and mathematical disciplines, apart from computer science.
 
3.    Business Skills - As data scientists wear multiple hats, they need to have strong business skills. A data scientist has to communicate with diverse people in an organization that includes communicating and understanding business requirements, application requirements and interpret the patterns and relationships mined from data to people in marketing group, product development teams, and corporate executives. And all this requires good business skills, to get the things done right.
 
4.    Visualization - The fourth set of skills focus on making products real and making data available to users. In other words, this one's a combination of coding skills, an ability to see where data can add value, and collaborating with teams to make these products a reality. You may be able to mine and model data, but are you able to visualize it? Well if not, mind that you should be able to work with some, at least a few of the data visualization tools. Some of these include Tableau, Flare, D3.js, Processing, Google Visualization API, and Raphael.js.
 
5.    Innovation - You don't just have to look around and do with data. You got to think creative, and innovate. A data scientist should be eager to learn more, be curious to find new things, and think out of the box. They should be focused on making products real and making perfectly done data available to users. They should be able to see where data can add value, and how it can brings better results.
 
6.    Problem-Solving Skills This may seem obvious, of course, because data science is all about solving problems. But a good data scientist must take the time to learn what problem needs to be solved, how the solution will deliver value, and how it'll be used and by whom.
 
7.    Communications Skills - Communication is the key to work with various cross-functional team members and present analytics in a compelling and effective manner to the leadership and customers. In other words, you may be brilliant in your rarefied field, but you're not going to be a really good data scientist if you can't communicate with the common folk.

 

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