5 Intriguing Facts about Data Science

5 Intriguing Facts about Data Science

1. Fundamentals:

We are well aware that the components of data science are data analysis, machine learning (ML), deep learning, and artificial intelligence (AI), which are intrinsic to furnishing humans with technology. In addition, it requires a basic understanding of statistics, linear algebra, calculus, analytics, and so forth. Although human brains are highly capable of creativity and innovation, we are cut out to perform efficiently only in conditions that are suitable for our senses. After some point, that is, out of our comfort zone, we are unable to do what machines can do in adverse situations. Hence, data science becomes of utmost importance for the future, provided next-generation technology needs data-driven inputs.

2. Leaps:

Technology has advanced in leaps and bounds over a decade as far as it goes. However, there is still a lot to achieve in order to get rid of hard toil from our lives. In this day and age, we use first-generation machines, which work in sync under our supervision at manufacturing plants, and second-generation machines, with an elementary level of intelligence, in our daily lives such as smartphones and computers. Nevertheless, we need third-generation machines that will have their own intelligence, with minimal to no supervision by humans, in order to be free of drudgery.

Stephen Hawking once proposed a project called “Breakthrough Starshot” or “Laser Sails,” which hasn’t set off yet, given that the project requires fifth-generation computers and self-replicating robots with a very high level of intelligence. With microchips placed on nanoships, which are powered by laser beams from Earth, this project requires considerable precision so that the nanoships do not veer off the set trajectory. The only problem with this project is that when laser beams travel through Earth’s atmosphere, they fail to retain 60% of their energy. To make it a success, scientists are planning to send a fleet of self-replicating robots on the moon to set up laser launch pads and fire laser beams so that 100% energy will be utilised.

This is just one kind of project, and there are other projects such as nuclear fission rockets, plasma engines, nuclear fusion rockets, ion engines, antimatter rockets, and solar sails that are in the minds of scientists from all over the world. Now imagine how much importance will be placed on data scientists to bring such technology into use. Hence, I would suggest all prospective data scientists fasten their seatbelts and be ready to witness a cascade of jobs.

3. How to be a data scientist?

A requirement for taking this challenge in our stride is that we invest in highly skilled data scientists. For being a data scientist, one must know that preferred educational qualifications in order of precedence are a PhD, then a Master’s degree in any branch of mathematics or IT. Having said that, a professional with a lot of technical experience and no educational qualification can also become a data scientist as long as he/she demonstrates the ability to find the underlying cause of business problems and hit upon a technological solution. On top of that, one must exude insatiable curiosity to be a data scientist.

4. Importance of data science:

Data science does not confine itself to one type of industry, considering it has become multimodal over time in view of its need across all types of industry sectors. If one is an exponent of programming, then he/she is going to have a promising future as a data scientist.

As an award-winning data scientist, Jim Gray, forecasted the metamorphosis of data science almost two decades ago, little did we know that data science would spark a data-driven revolution into the world of information technology. He visualised how data science will become a paradigm shift, which will work wonders across all types of industry sectors with a view to improving customer service and customer experience as well as removing time constraints.

Now, we can have a barrage of data at our fingertips, but the main obstacle is that the preliminary data is not clean and precise. It needs to be segregated into training and testing sets with a view to obtaining the technological upshot of business problems. More importantly, a data scientist must be persevering by nature because a lot of his/her time is spent on data cleaning.

5. Nextech Infosystem’s Contribution:

There is a wide range of intensive programmes that are being offered by Nextech Infosystems such as Machine Learning, Python, Big Data – Hadoop, Spark, Data Visualisation, and NLP, which are sufficient to get your hands on data science because Nextech Infosystems not only provides data science courses and job-search assistance but also facilitate engagement with the data science community. Most importantly, the team of Nextech Infosystems has taken the responsibility upon itself to churn out fully equipped data scientists so that a better world can be created.