There are a group of people who spend time with data all day long, analyze data and build relationships, form patterns and identify trends. They take the seemingly random data, clean it up and analyze it to get some useful information out of it. These results help organizations make smarter decisions to improve the overall performance of the company. Data science is a mix of many scientific fields of study like statistics, mathematics, business intelligence and even computer science. It also has a place for emerging fields like Machine learning and Artificial intelligence. Again, you should know and have a comparison between Data science and Artificial Intelligence and similarly Data science vs Machine learning, so that you can become that much more efficient as a data scientist. Data has become the most important factor for decision making across all industries. This has, in turn, increased the demand for all jobs associated with handling data. The data scientist is one such role where the demand is still exceeding the supply. This investing in becoming a skilled data scientist is a great move career wise.
The 'data scientist' is relatively a new term for the job market. But even before that, there were people who were working with data like data engineers and data analysts. So, what is the difference between these terms? How does each of this post and job description affect the other? Which is the better choice? are all the questions that we face during a job search. The roles are kind of related but you need to find out that is a better fit for your skills. Data analyst is usually where most people end up when thinking of a career based on data. You need strong statistical skills, understanding business and work with numerics to be a good fit for this role. A data engineer works with technical aspects of the data analysis. You need to be able to create and integrate APIs for handling the data as well as prepare for complete optimization for a company. A data scientist does all this and more. You need good education qualification as well as significant practical experience to become a data scientist. They handle big data and work with, analyze and interpret complex data. Of these roles, it is the data scientist who gets paid the big bucks. Many times, the data scientist with a computer science background or skills is also capable of doing the work of the others making those jobs redundant. So if you are here looking to get into the field of data science, check out our data science guide for beginners.
The salary of a data scientist is typically above the average salary paid for other jobs with the same level of experience. The salary of any position, including that of the data scientist is dependent on mainly the job description. You get paid a salary for your contribution to the growth of the company. So, to get a good salary, you have to match the employer expectation on skills and experience. Let us take a look at the salary of a data scientist in India and a few other English speaking countries. The amounts are all over the place, with some countries paying a lot more in terms of others if you put them on the scale. On a closer look, you can see that as a whole, data scientists do get paid higher than every country's national average pay for the level of skill and experience. Every company has a set of metrics based on which they decide upon a salary and we'll soon be checking out a few of them.
The average salary of a data scientist in the US approximately USD 128,000/-*per year. While in Australia, the salary of a data scientist is around AUD 120,000/-* per annum. The UK shows the figure of EURO 54,000/-* per annum. * Source: Indeed. All the salaries average and highest are taken from the set of salaries that were reported to and collected by Indeed .
The national average salary of a data scientist in India is Rs. 7,00,000/-* . The highest recorded salary of a data scientist is 1.7M per year for an experienced data scientist. *Source: Payscale. All the salaries average and highest are taken from the set of salaries that were reported to and collected by Payscale.
There are a few very important factors that influence the salary of a data scientist. Some are general factors and other factors are specific to the field of data science. Here, we discuss some of the salary influencing factors.
Location There are some very general factors that have no bearing on the individual, and yet is a factor in deciding the salary of a data scientist. That is the location of the job. Often, a job posting in a cosmopolitan and metropolitan pays more than the smaller cities and town. The industry-oriented cities often have a higher pay scale compared to some other cities for the same job, skill and experience. Employer There are employers across a multitude of industries. Some pay higher than the average pay but most stick to the average salary. Depending upon your experience and skills that they require, the salary is likely to vary. The salary of a data scientist is above average compared to others and that is not going to change any time soon with most employers willing to shell a good amount as a salary for the right fit. Education and Experience A good education is almost always necessary for a great salary. If you don't have the degree to speak for you, the best way to polish your resume to reflect your skills is by getting certified for skills. This is especially true for data scientists. Every employer wants the best workers, irrespective of the educational background. So if you are one of those who don't have a degree from the premium institutions, then you should work on upskilling and making yourself job-ready. There are online courses on data science that gives you a platform to learn the concepts and apply them in real-time situations. Classroom-oriented offline courses on data science will also be available in your city, which is a good choice if you prefer going to the class and have a disciplined and structured learning experience. Such certifications will improve your standing with a prospective employer. Other than education, experience and practical knowledge is a powerful influencing factor in landing the perfect job with a great salary. Negotiation This is less of a factor and more of a skill. Your prospective employer will always have buffer space for a salary increase when they give you their initial offer. So, when an employer comes calling, don't settle for your the salary offered. Try to bank on your skills and negotiate towards a reasonable salary for both parties. remember that negotiating does not mean you demand some randomly quoted high figure. analyze the market rate, salary trends and your skillset and then go for negotiating a good salary. As a data scientist, this is a way to be better paid than your peers who all showcase the same set of skills.
Tools used This is a transient factor and a very important one. Apart from your experience and skills, the tools with which you work are also taken into consideration for a job as a data scientist. mostly, the tools that are trending keep changing and you need to keep yourself updated along with the times. But some of the fundamental and popular data science tools are still relevant today and learning them along with trending ones give a unique character to your job application. This also means that the programming language that you know influences your salary. As of 2019, Python is on the hot skill list and if you want to be on top of skills, then you should learn to code with Python. Industry An important factor that influences salary is the industry for which you work. Some industries have a greater dependence on data and data-driven decision than others. Thus, the salary of a data scientist is expected to vary depending upon the work and dependency. Roles make sure that the job you apply for holds the job title Data Scientist rather than a Data Analyst or a Data engineer or other data-related positions. To differentiate between the roles, look for the specific match in the job title. In terms of work, the job description will guide you in selecting the right post. Go through your strengths and find a company that requires most of your strength and skills. These will be detailed in the job profile and expectation from the company on the qualification. The posts have a clearly defined requirements for reach role and these will be explicitly mentioned in the job description.