Big Data can be simply termed as the large sets of data which are not easily analysed with the help of traditional data processing methods. It has become a common term nowadays, with both big and medium-sized organisations understanding its importance when it comes to business growth. With the help of big data, companies can gather better customer intelligence and insights into the market behaviour.

To understand the importance of big data, its future impact and what are the prospects for aspiring big data professionals, we have interviewed one of the experts in the industry, Mr Rajasekhar Reddy Neerubai. Here is what he shared with us:

Q1. How does data science differ from traditional statistical analysis?

Regular statistical analysis relies more on available current data, reporting them in the format needed, and analysing what has happened so far.

Data science predicts outcomes based on available data. New algorithms are built using techniques like what-if analysis etc. It is also called by many names like data mining, machine learning, and artificial intelligence. The quality of data is a must. The better the data is, the higher is the accuracy of the predictions.

Q2. Which industries give more preference to Big Data analysts?

Market research companies are on the lookout for talented big data professionals. With the use of technology in elections nowadays, Psephologists (who conduct election voting pattern research) having big data experience are also in demand. Medical research companies also are rising on the list of industries that are looking for good big data analysts.

Q3. Which industries will see the most impact from Big Data in the near future?

E-commerce companies will see the most impact from big data in the coming years.

Q4. We have seen a lot of profiles like Data Analysts, Data Scientists and Data Engineer? What are the differences?

Data Analysts and Data Scientists need to have business domain knowledge whereas Data Engineers need to have technical knowledge, including the infrastructure.

Q5. How much time and resource does it require for one to successfully shift to a Data Scientist profile from a Software Engineer?

For a person to successfully shift from a Software Engineer to a Data Scientist profile, it depends on how much knowledge of statistics the person has.

Q6. Which is better for data analysis: R or Python? Why?

I didn’t know much about Python. But I have learned a little bit of R language. It is quite easy to learn R. It can be safely said that one can learn both. If you are new to both, then you can always start with R and then learn Python as well.

Q7. What are the essential skills that are necessary to be an effective Data Scientist?

To become an effective Data Scientist, statistical knowledge is very essential. A little bit of database-related skill is also very helpful.

Q8. What is the best advice that you can give for someone to be successful in their Big Data career?

Keep learning new ways of handling data.

Q9. Is it possible to make a late-career shift to a Big Data profile? What steps do you suggest should one take to make the shift?

If you have the appetite to handle a huge amount of data and like to work with data, then why not? When you have the passion, feel easy to work with data and have the basic industry knowledge, nobody can stop you from becoming an expert in big data. However, it would be easier if you can get certified or take a course in big data.

Q10. Are there any new trends in the Big Data industry that we should be aware of? How is Big Data going to evolve in the future?

The future evolution of big data industry is going to be towards Cognitive Analytics, like IBM Watson.

With cognitive analytics, data analysis is going to be automated and will bring a high level of fluidity to the big data industry.

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