We live in a world driven by flood of data. To manage it means a hell of a job. The person needs to utilize his/her knowledge to convert data into actionable insights about everything from product development to customer retention to new business opportunities.
All kinds of organizations need data scientists to deal with the developing scenario.
The way an organization approaches and deals with big data and cloud technology, makes a big difference in the ability to compete in the future.
The current data processing application software finds itself inadequate to deal with the complexities of big data. Some challenges to be faced include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.

What Data Scientist needs to do

Because the field is growing so fast, a data scientist needs diversity, artistic freedom and adaptability. The data can be integrated with any other measured activity that is done throughout the day. There is no end to the possibilities with the amount of data already available. And there is no right or wrong way of doing the job, it is about the impact, the work has on other people. You can take the role to whatever field you want, because data is everywhere.
The data scientists should feel the rhythm, get into the groove and harmonize a solution. This is possible with the right amount of freedom and adaptability only. Tools and techniques will come to mind automatically.
The role of a data scientist is fast becoming the most sought after career in this technology world. The big companies like Google, Facebook, Amazon and LinkedIn are using data scientists to help them maintain the innovative edge in the digital data era.
A data scientist always tries to bring new ways of measuring and displaying data so that more awareness, clarity and direction is provided to those having the feel of it.

Skills Needed to Succeed

It is hard to find a data scientist who possess technical, analytical, and also presentation skills. They should possess knowledge of statistics, applied mathematics and enough programming to engineer methods for sourcing, processing, and storing their data. And the biggest skill is to communicate their findings through data visualizations and stories.
Following languages and applications required are SQL, R, Python, SPSS, Tableau, and Hadoop.

1. Right Skills

• Practical computing skills
• Statistical skills
• Feel hungry to learn

2. Make Connections

The most important thing to get in any field is to get learning. Then it is time to make some right connections, because being part of the data science community is the fastest way to know what you do not know. It matters in a field, that is coming up very fast.

3. Have a look Around

Approach the data science field with an open mind. Think critically. You never know which pile of raw data will turn the tables.

Conclusion

A data scientist’s goal is to create massive amounts of value for the largest number of people possible. While a data scientist works behind the scenes, it’s not unlike playing to a large audience: the better you do the job, the more people you reach – and the more rewards you get.