Lately, all sorts of social media platforms these days have really started to pop up with advertisements regarding data science with catchy titles such as “Master data science in 3 months”, “Learn advanced data science”, etc. And let’s be honest here, a lot of us are actually somehow magnetically attracted towards the term ‘data science’ regardless of whether we know anything about it or not.
To begin with, data science is not a field that works totally alone and isolated. It is actually just a concept and not a tool or framework that can actually achieve something. As a concept, it is implemented through various tools and services falling under the umbrella of technologies such as Big Data, Machine Learning, Data Mining, etc. These technologies in return are implemented through vast libraries of powerful coding languages such as Python, R, Java, etc. The world population on an average creates data somewhere in exabytes (10^10 GB) on a daily basis. For 9 out of 10 people, this is nothing but just waste, but for data science and its professionals, this heap of completely random data somewhere within it packs the answer to some of the biggest problems that their association/business/organization is facing.
Lowdown On Data Science Technologies
As said earlier, data science is just a concept in itself and is actually implemented through various technologies. Let us list and understand some of them.
Big Data
One of the most quintessential technologies involved in the success that data science is enjoying at the moment is Big Data. This data can be structured, unstructured, or semi-structured in format, and in most practical scenarios is a combination of all the three. It is so large in its size that traditional databases and software are rendered completely incapable of processing them and so to tackle the very same problem, Big Data comes into play. It makes use of tools such as Hadoop, MapReduce, distributed computing, etc. Big Data as technology is mostly implemented through code is written in one of the oldest, popular, and beloved languages in history- Java.
Machine Learning
Machine learning is all about taking the next step towards making Terminator a reality. Machine learning tools and libraries take up an entire dataset and try to find out the thing of interest using the underlying algorithms and consecutively use the findings to come up with a result of value. This technology is completely implemented through either Python or R as the programming language.
Prospects For Data Science Professionals
The industry of data science is currently running really short of professionals who can bring about the changes that are visible and impactful. In order to get their hands on such individuals, the biggest of companies and IT giants are willing to splash the cash like anything; meaning that if the world of data science fascinates you and you are looking to make a career out of it, now is the best time to strike the hammer while the rod is still hot.
Challenges Ahead
Data science is a relatively new field and is a work in progress. Some of these challenges and hurdles in the way of data science reaching its true potential are:
- The first and the biggest problem with any data science project is finding and creating the right data set for it. As we discussed and saw that the world generates exabytes of data in a single day, it would be blatantly stupid to even think that they are collected and exist in a proper format. They are just chunks of random data that have absolutely no correlation to one another. This makes filtering out these truckloads of data as per the needs of the project a significantly difficult task for the professionals to carry out.
- Data science is practically a concept that allows one to predict the future of an event-based upon its past experiences. In order to achieve this, data science professionals design what are called models. These models are deployed on a particular coding language. The very purpose of these models is to solve a particular problem in real-time. There are 2 problems with designing a model-
- First is that no one model can be used to solve the problems being faced by another model. One model can just be used by another model if it wants to solve the same problem that has already been solved by it.
- The second is that model designing requires extreme levels of thinking on the feet, creative thinking, and out of the box ideas and the lack of professionals in the domain often leads to poor models with even lower levels of precision and accuracy in them.
- Another big problem with data science is that it is the new kid on the block. Businesses and firms have no doubt understood that affiliating themselves with data science is imperative in order to sustain themselves in the near and the far future.
