If you ask anyone what data science mean, some of them might just answer it in one word ‘Big Data’ while others might be knowing the complete concept of Data science but won’t be knowing how to define it exactly. While few might be having the idea that it is something connected to statistics. Since data has started exploding, statistics has obviously become such an indispensable measure in data science.

The statistic is never a far cry from data science. It is used to escort and guide the data that is stored in the databases. Someone who can do a whole lot to process the data such as statistics, programming etc. is called as the data scientist.

People often select someone who is a Hadoop engineer or someone efficient in SQL as the data scientists. Those people can obviously handle with data science, but they are never scientists. The complete in and out knowledge of statistics is a commitment to data science. The misstep and missing of data from the databases need full skilled statistical learning. They exactly know what problem they are going to face and what solutions they need to come up with.

With a strong technological upbringing and towering knowledge about data is a precondition. Data scientists find out what every piece of data means and do that really makes sense. Data distribution and modeling of the available data, finding out small mistakes in data arrangement are the periodic job of the data scientists and they exactly know what to do with them in contrasting situations and circumstances.

Assessment, measurement, dissemination and certain statistical tests should be very much clear for a data scientist aspirant.Only knowing all the techniques is not important. But knowing when to use them holds much significance. You need statistics to work as a data scientists in every company.

## Analyze Properly

Data visualization and analyzing them and explaining them to both technical and non-technical people is so much important. Communication skill is incredibly important for data scientist working in smaller companies as well as in multinational ones.

## Be Excellent in Your Plus and Minus

Efficient in mathematics such as calculus and algebra is a must qualification to work in data science. Mathematics is the base of statistics and to study and research on anything you have to have an in-depth knowledge of the underlying of the particular subject and all other subjects related to it. Understanding the concepts and their expansions will help you in the research and the invention of new expansions and extensions one would be performing during trial and processes.

## Instrumental Knowledge

Machine learning methods such as random forests, nearest neighbors etc are most fundamental if you want to work as a data scientist in a company which is mostly driven by data. Understanding the use and when to use broad strokes makes a very big difference in data science. R and Python languages are so much important to be a data expert.

## Be Ready for the Sloppy Stuff

Messy and overflooded data and dealing with the complications in the data are the daily routines of the data scientists. The freshness of data, streaming, proper movement all is to be taken care of by the same.

## Programming Always Helps

Once you are into data science you have to be belly fully efficient in logging and metrics. Programming and software analysis is always an added experience whether it be data science or geoscience. All that has to be done is done in the computer systems. So being a good player of software always makes a great deal.

Always learn to face problems never run away from them. Problems will always come in your career as a data scientist. Be an ultimate problem solver and you will definitely win your journey in the world of data science.

Aakash says

Nice Article, Can you through some lights on Types of Big Data Technologies.

Atul says

At this moment there are 4 kinds of Big Data Technologies,

1. Prescriptive– This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps.2. Predictive– An analysis of likely scenarios of what might happen. The deliverables are usually a predictive forecast.3. Diagnostic– A look at past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.4. Descriptive– What is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard and/or email reports.Paras says

Very nice article. Nowadays Data Science is the fastest growing technology.

Siya says

Hello, really awesome stuff. Keep up with the good work.

gupta says

Hi Atul, Thanks for sharing nice information it really helped me a lot.