There exist many professions in the world, each being a cog in the wheel in the intricate machine that is the world — moving at their paces and contributing at their potential in making the world a better place to live. However, the fuel required to run this machine and power the whole system is “technology.” Technology has always been at the forefront of almost every profession known to man. From the inception of the world, humankind has evolved from having spears and swords in their hands to having digital pens and mobile devices instead. This evolution of technology has helped the human race to progress at an exponential rate.
Today, the world continues to explore new ways to ease the burden that humanity has borne over its back, by providing innovative opportunities to gain the knowledge of this world, its horizons and beyond. Consequently, new professions are continually emerging, which manage and operate the existing development of the society.
Amongst some of these emerging professions of this era, a need has been observed for people who can make use of massive amounts of data that is generated every day. These people are expected to collect, analyze, and interpret datasets, bit by bit, to unveil meaningful patterns in them. “Big Data” is the massive amount of structured or unstructured data that inundates a business on a daily basis which therefore can’t be processed through traditional data processing techniques.
Two major professions deal with big data which are rather defined vaguely and sometimes used interchangeably with one another, clearly undermining the clear distinction between them. These two professions are that of a “Data Analyst” and a “Data Scientist.” Both of these professions require a different set of skills from someone to hold its title. Here are some of the distinctions between both occupations in terms of what they do, and where are they needed.
There’s a significant difference between the skillset both the professions need and amount of knowledge in fields of communication, statistics, business, and mathematics.
Data analysts peruse a large amount of data and draw insights from it. They are responsible for collecting data, organizing it, and obtaining statistical explanation out of it. These explanations are given through visualizations and reports so that businesses can make strategic decisions out of them.
A data scientist, unlike data analyst, can make predictions which help businesses make accurate decisions. They can do nearly all the tasks a data analyst can do as they too are proficient in mathematical, statistical, and computer application skills. They are the ones who are efficient in picking and solving the right problems that can help a business excel.
There are numerous similarities between data scientists and data analysts in terms of the expertise they offer; however, there are some primary traits which give data scientists superiority over data analysts.
They need a basic understanding of algorithms, mathematics, statistics, and it’s preferred if they’ve excellent communication skills and considerable knowledge of computer science. Data analysts are masters in SQL and with common expressions can easily dissect data and devise meaning out of it. Moreover, they’re required to help organizations make decisions by making sense of patterns and trends found in the datasets.
Apart from data storing and retrieving skills, they’re expected to have an understanding of Hadoop based Analytics, such as HBase, Hive, Impala, Cascading, and a much famous tool Google Analytics. Furthermore, they need to be familiar with various ETL tools for transforming data from different sources into Analytics Data Stores.
A data scientist possesses all the skills of a data analyst, with profound knowledge of modeling, statistics, mathematics, and computer science. Having super skills in Math, Stats and Data Mining, data scientists are experts at predictive modeling and Machine Learning. So, they can create complex predictive models which can give valid recommendations based on historical data.
The differentiating factor, however, is the robust business acumen that sets them apart from analysts. It is necessary for a data scientist to be able to convey the findings in the form of a story to both, the IT professionals and the business managers so that they can take calculated risks and make viable decisions based on the information provided by the data scientist.
Today, data analysts are in high demand in the healthcare, travel, and IT industry. They are also actively involved in data crunching and analytics work in all sorts of businesses.
Data scientists, on the other hand, are commonly required in E-commerce, Search Engine Optimization (SEO) and Financial Services for companies. Furthermore, they’re needed by businesses to predict future trends of the industry from the existing data and make logical decisions based on those future trends.
For every profession, salary is a major concern for one’s interest to pursue a career. No doubt, salaries offered to data analysts and data scientists are competitive in the data industry; nevertheless, data scientists earn more than data analysts due to the level of expertise they offer in their field.
This difference is due to the knowledge base, expertise, and experience required by both professions.
Regardless of the differences and similarities, both of these professions play a pivotal role in their places, and one can’t go wrong with either.
A study in 2018 by PWC predicted that
The study further elaborates that the ideal candidate must be T-shaped, which means that alongside having analytical and technical skills, he/she must also possess “soft skills” such as excellent communication, teamwork, and creativity. So, it will be wise for the prospective candidates out there to develop strong analytical, technical, and soft skills to prove their worth to businesses who are desperately searching for data handlers for their company.