A succinct introduction to Data Science careers.
Inside this modern technophile name DATA SCIENCE lies the archaic desire to organize the gigantic amount of data created by us and finding better methodologies and engineering tools to analyze and make use of the data produced to drive decisions in the proper course. All the data crunching methods require an extensive amount of people in data mastery making the process more efficient.
Data is omnipresent. We are in a time where digital data is augmenting rapidly. This is changing the way industries, be it the healthcare, financial or manufacturing operations. It has thus created the need to examine the available data and draw meaningful insights for organizations to advance and flourish. Along these lines, the interest in Data Analysis and Data Science is augmenting amongst almost all the sectors. While these roles share some parallels, there are specific skill sets that a person must acquire to adept in the particular domain. Hence comes the encoded trending careers in data namely Business Analyst, Data Analyst, Data Engineer, and Data Scientist. These terms are used interchangeably frequently as there are no fixed definitions. I analyzed a few job postings on LinkedIn to understand what most of the companies expect from each of these roles.
Business Analysts basically extract valuable information from structured and unstructured sources to explain the current and future business performance. They help in analyzing the best model and providing solutions to business users. They work mostly for defining business problems and translating the analysis into data-driven business intelligence that improves business performance.
A Data Analyst is basically a novice Data Scientist which is probably a position to inchoate your career in Data Science. The basic responsibility is interpretation and alteration of prevailing data sets, looking out for patterns and bringing out conclusions. In order to perform the aforementioned tasks, a rudimentary knowledge of data munging, data visualization and statistics is mostly expected. Apart from this, to present findings to non-technical people, knowledge of simplifying complex data to ad-hoc reports and charts using visualization tools is also necessary.
A Data Engineer focuses on the hardware which assists the data-driven activities. They are responsible for maintaining, expanding and improvising it whenever necessary in order to increase the competence. They are basically the back-end workers which help the Data Scientists and Analysts to do their roles meritoriously.
A Data Scientist is dissimilar to a Data Analyst in terms of Skill Set and Experience. At this Position the person will be confronting a larger Volume, Velocity, and Variety of Data. Bagged up with the knowledge of tools to alter current Data Sets they can invent new Algorithms as well, in order to ameliorate the efficiency in solving Data Problems. Usually, data scientists have collaborative and niche knowledge about business as well as details about cutting-edge data visualization skills. The skills required are highly variable and largely dependent on the type of business they are involved in or the problem they are trying to solve.
Bonus: Here’s how much these roles get paid on average as per Glassdoor.
An analytical thinker who believes in searching story behind data.Aspiring to become a data scientist, I am interested in utilizing statistical and data mining techniques to enable businesses to expand to new horizons and to provide insightful solutions to challenging business conditions.
If Data is the oil of 21st century, then analytics is the combustion engine!