The 21st century has apparently witnessed a massive digital revolution that not only made lives trouble-free but has also driven mankind to take a gigantic step towards simplifying their everyday chores. As the future seems pretty evident with the booming of enormous advanced technical equipment every passing day, the digital bug has already bitten the tech-savvy generation with sheer force and rapidity.
When it comes to business sectors, digitization and automation hit different domains with the advent of huge piles of unstructured or unprocessed data. As per the study, during the 1980s and 90s, most of the data were structured largely because they were small in size which could be easily processed with the help of simple Business Intelligence (BI) tools. However, by early 2000 the data accumulated by organizations became unstructured because they were large in numbers which were certainly difficult to analyse using the BI method.
Here, comes the role of data science to systematise the data of various business ventures using advanced tools and algorithms. Let’s shed some light on the meaning of data science.
What is Data Science?
Since the exponential growth of big data in business activities, the need for its storage also grew fast. But slowly the focus shifted from data storage to analysing the huge amounts of data for making better decisions. In conclusion, it can be said that data science is the process of observing and analysing hidden patterns of data to get meaningful business insights. Data Science is the future of business because it can be applied in all sectors like finance, commerce, health etc. which has the power to change the dynamics more beneficially.
On the other hand, a data scientist is someone who gets involved in studying, processing and analysing data. He is basically the captain in an organization who has the power to take the business to a higher level predicting different possible outcomes. When we say data, it can be generated from multiple sources like multimedia forms, financial logs, digital media, large text files etc. To process such huge volumes of data, we need state-of-the-art technologies that aid in drawing concrete conclusions out of it.
Although data science has been in the business scenario for a long period of time, its popularity in India has only increased recently. When the COVID induced lockdown succumbed the entire world into nothingness; people started exploring inordinate ideas as a method to distract their minds. Many people joined for different courses to foster productivity and the data science course was one among them. There is a well-ordered process involved in becoming a data scientist which should be strictly followed for the smooth transition into this domain
How to become a data scientist and how long does it take?
In Data Science, the primary focus is given to machine learning, which helps make future predictions from the previous data. For instance, the recommendation that a customer receives after buying a product from e-commerce platforms like Amazon or Flipkart is the simplest way of explaining machine learning algorithms in a layman’s language. Here, the model captures the order history from the customer’s database and based on that release recommendation to create a buying tendency in his mind. These kinds of models make it user-friendly which has definitely contributed to the popularity of these online shopping sites.
The first step towards learning data science is to get through with all prominent topics like data science foundation, machine learning, R programming, python, tableau etc. Machine Learning is pertinent in the study of data science as both of them work in binary opposition. While data science is the study of data but machine learning is the instrument used to carve out insights from raw data and make predictions about an event that is going to happen.
In a nutshell, data science is a tool to make corporate decisions and a machine learning algorithm is for making prophecies about a particular event using predictive and prescriptive analytics.
The next relevant aspect is to engage in capstone and client projects by collaborating with a good Artificial Intelligence (AI) company for getting hands-on experience. There are different predictive models like email spam detection, flight fare prediction, bank loan risk prediction, breast cancer detection etc. which gives suitable practical knowledge from diverse industries. Most importantly, data science requires practice by working in real-time projects to broaden the theoretical knowledge of a data enthusiast.
After working on industry-based projects, the International Association for Business Analytics Certification (IABAC) exam is mandatory to become a Certified Data Scientist. IABAC is a certification body that is exclusively dedicated to data science aspirants providing them with a lifetime certificate that has global recognition. It conducts a Data Science Foundation exam that is MCQ based and a project which should be completed within 15 days of the commencement. Subsequently, candidates will be awarded certification and they will be recognized as a Certified Data Scientists.
A Certified Data Scientist can apply for multiple job positions like data scientist, data analyst, data engineer, business analyst etc. that will open doors to work in all industries irrespective of the educational background they possess. For instance, an HR professional switching into data science can work in any sector apart from the HR-oriented domain. This provides numerous chances to explore different fields making them gain expertise in all major industries.
Many top reputed institutes provide six months to one year to complete this course. But ideally, six months is more than sufficient to do a data science course lest become complicated. During a time when the whole world has moved to the digital medium, there is also an option to enrol for the sessions virtually.
Data science or Certified Data Scientist Program is one of the sought after job oriented courses of the 21st century which is designed for working professionals and students who are just out of college. It has the potential to take the corporate culture to a next level incorporating all the modern mechanisms as well as impart relevant skills and knowledge to the budding talents that resonate with the current setup.