But in some fields, even small data analysts can reach that level of measurement; According to a recent study by Springboard, Data analysts in natural resources and mining can expect upward wages of $ 100,000, while those working in technology, science, and technology earn an average of $ 90,000. Those employed in the financial and insurance sectors are estimated at $ 90,000 — which includes an estimated 400,000 jobs.

How Much Money Can Data Professionals Make?

There is evidence that for people who are already working in the field, increasing your skills in certain areas can give your salary another important boost. According to an IBM report, MapReduce data analytics experts draw annual revenue of more than $ 115,000, while professionals with experience using Apache Pig, Hive, and Hadoop are all in the job market with more than $ 110,000 per year.

According to IBM, the right certificate can open many doors. Their report stated that if the forecast for “analytical” employees appears to be correct, almost all of those employees will need to change jobs every year to fill the required data analysis and data science positions – making profits increase exponentially. the most likely travel.

Skills required for Profile Analyst profile:

1. Understanding a Solid Domain

Data Matters Skills help solve problems. They need to understand the dynamics of the business, the levels they may be able to deliver to bring about significant positive changes, external and internal factors that affect its growth, and to demonstrate all the necessary decisions appropriately. Understanding the business you should have is one of the most important skills if you wish to become a data analyst and directly affect the income of a data analyst in India.

2. Statistics & Statistics

Targeted decision-making is a very important part of how you come to a solution for any decision. To make good decisions you have to rely on Mathematics and math. You need to find patterns, parts, make predictions based on historical information. You will need to use prediction algorithms, divisions, and integration algorithms to get to the best solution, which is where Mathematics and Mathematics will deliver.

3. Technical Skills

You can identify and solve a problem using background understanding and mathematical skills. But most businesses are not so simple. You will need to go through data sets beyond your computational skills and to be able to duplicate your algorithms and business solutions on a scale, you must take tools like R, Python, SQL, etc.

4. Soft Skills

Lastly, but not least of the data analysis skills, you should be able to communicate your solution in a simple and understandable format for participants. They may not know anything about KS Statistics, the square root error, or your integration algorithm but that is where your soft skills come into play. Effective communication, good viewing usage, and viewing tools like Tableau, QlikView, etc., have become really important.