Jobs in the niche of data science is quite lucrative in terms of fat-pay package and adequate job exposure. But the role of a data scientist may slightly vary from one company to other. For example, in the Indian job market, almost every Java software development company that deals with data science and data management, recruits data scientists but the role may vary depending on the nature of data the company deals with. This year unveils enormous job prospects for data scientist provided they acquire these four competencies.
Proficiency in quantitative analysis
Quantitative analysis is one of the most pivotal skills of a data scientist which helps him in acquiring knowledge about a set of data for three major reasons. These are
- Experimental design and concerning analysis: This mode of analysis works for consumer market-related data, where a data scientist can help a lot.
- Machine learning: This is one of the most intricate roles a data scientist plays. A data scientist helps in creating prototypes for testing assumptions, selection and creation of needed features, as well as he helps in recognizing the areas of strength and prospect in obtainable machine learning systems.
- Simulation of complex economic or growth system: In this area, a data scientist checks relevant data and analyzes the prospect of the growth of a model in accordance to a specific infrastructure.
Timely and proficient communication
Timely and proficient communication is one of the most important skills expected from a data scientist. His ability to insightful communicate can help him in three ways:
- Offering business insight for a data set so that the other department of the company can understand the analysis and use it for further product or service planning
- Data visualization is done by a graph as well as infographics, which help others the practical utility of the data analysis and the way to use it at its best for the growth of the company.
- A data scientist needs to collaborate with different departments and different levels of professionals in a company. Good quality communication power helps him to explain his result of analysis for others.
A solid command over programming
Command over programming helps a data scientist to apply his skill in multiple ways, including:
- The ability to analyze large volumes of data
- The ability to create tool for better data management with the help of data science
- Applying the ability of programming to implement the rules of statistics.
Ability to create product using your intuition
The competency to use your intuition is one of the abilities of a data scientist can apply to quantitative analysis on a target system. Product knowledge includes an understanding of the multifaceted system that creates the data which is analyzed by the data scientist. This is vital for a few reasons, which include:
- Creating hypotheses: Data scientist should be able to generate hypotheses. He is expected to identify a specific product properly so that he can create hypotheses about the ways the concerned system can behave if it is changed in a specific manner.
- Defining matrices- A data scientist should have adequate knowledge about a product so that he can create relevant product metrics that can measure, which is projected, as well as what is worth moving.
- Debugging analyses: This is simply done on experience and logical intuition. If a data scientist has good product knowledge, he can check all the results of the analysis and especially the unusually best one and use his ability can identify the wrong areas.
These are the best competencies of a data scientist who want to face the challenge of the 2017-18 job market. According to recruiters, the competencies described here are expected to bring good job prospects, a lucrative salary, and brilliant job exposure for data scientists.
