: Textual data files with a discernable patte : Textual data with erratic data f

Question

: Textual data files with a discernable patte : Textual data with erratic data formats, can 10 ( enabling parsing formatted with effort, tools, and time. ): Data that has no inherent structure and is 12. stored as different types of files Word Bank big data Analysis flexibility, Analytic Sandbox, BI Business Intelligence, Data Mining, Hregh throughput, Predictive Analytics, Speed of decision making Framing "Quasi" Structured, Semi-Structured, Structured, Unstructured Key Performance Indicators (KPIs) B. Type II Fill in the blanks (3 points) EDW stands for ( BI stands for ( OLAP stands for ( ) tasks. OLTP stands for ( C. Answer the following questions. (10 points) Write 5 main competency and behavioral characteristics for Data Scientist

Explanation / Answer

Solution:

These are the Five most important competency and behavioral characteristics of Data Scientist:

1. Business Understanding : This is one of the most important characteristic of the data scientists as a good data analyst may not be a good business planner. Creating models based on some statistical analysis on a huge data alone cant not make a company successful and understanding of the company goals and objectives is very important. Unless a DS has good understanding of the business, he can not be a good data scientist. He can be called as data analyst and not data scientist.

2. Passion for work: This is more of behavioral characteristics of a good worker. Unless he has passion for the work he does, he cannot be an achiever. Giving a solution for a problem is not the company looks for but a consistent growth and improvement in the solution quality is highly important.

3. Curiosity for learning New things: Finding solutions in a unique, easier and economic way for any problem is very important in this competitive world. Optimizing the existing solutions is one of the motivating factor for the data scientists. It enables them for new solution every day.

4. Innovation at work: Innovation is very important in this never-before competitive world. You need to be innovative at work to think out of the box. They must always be looking for the next big thing that will distinguish their offering from others already in the market.

5. Intuition: Mathematics and statistics is important for a good data scientists but some times intuition for market trend is very helpful in predicting the future of the market. The data scientist must be able to differentiate great from not-so-great analytics.

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