# Qualification needed for data scientist:

A data scientist is the person who draws a connection from the business world to the data world. They use their data, statistical and hacking abilities to find the rich data science-They often produce the answers for risky business related questions. There are no qualification criteria for the data scientist, but they need to have some strong background for the company preferences. Some companies mentioned the candidate must have MS degree and PhD is preferable. Because the company knows that MS/PhD graduates may have more knowledge on research involved datasets, can adapt quickly and also they have well trained in statistical skills through their academic projects.

The statistics show that there is an enormous growth in data science and there is gradual increase in the entry level data scientist. The experienced data scientist training the fresher for a few months before the complex project gets assigned. The data scientist salary would be approximately $119,000. Based on the performance, experience and growth of the company the data scientist pay will get incremented.

Suppose, if you already got an intership/ job offer in the data science field, then you are not supposed to do any higher degree. Just you need to gain the skills and then join the masters program if needed.

## Data scientist at google

For example, the high-paying companies such as Google, they employs the lots of PhD in statistics. Also, the PhDs are probably preferred because they complete the academic literature in his/her subject in a depth. But, in the data science field this expertise is not needed in most of the areas. They just needed skillfully works with the basic ideas.

## More UG’s are entering into a data science:

Within the next 5 years, there are lots of entry-level data scientists will come out directly from the Bachelor’s program. Because there are more resources and relevant courses are updated for the reference of students.

# Skills required for the data scientist/ data analyst:

**Programming skills**: No matter what sort of business it is, you’re likely going to be required to know how to utilize the tools to make the business profitable. This means using the statistical programming languages such as R, python and databases.

**Statistics skills:** A statistics knowledge is imperative for data scientist. It is a vital importance in all the businesses, however, particularly data-driven companies where they are not only focusing on the data, but also they will check the design and decision making capabilities of the data analyst.

**Calculus and Linear Algebra:** You must know about the complete mathematical derivatives. It is essential to know about multi-variable calculus and linear algebra, since they frame the premise because they dealt with lots of these techniques. You may ask why a data scientist would need to this stuff if there are lots of skills are there. The answer is simple, it can get to be justified, despite all the trouble for a data science team to construct their own implementations. Understanding these ideas is most critical in companies where the data scientist will be able to understand easily and predict the algorithm for the company’s profit.

**Data Visualization and Communication:** In companies where the data scientists are seen as the people who help others on data-driven decisions.