The evolution of data science has led to a rapid transformation in various sectors and domains. The wide range of applications of data science have become possible because of the insights and analysis provided by this technology to emerging enterprises. Various types of data science tools, techniques and methodologies have been harnessed by organizations to leverage the power of data and improve their growth prospects. The power of big data including artificial intelligence has been utilized for the purpose of pattern recognition and anomaly detection.
It has also given impetus to conversational AI and autonomous systems. In one word, data science has left its footprint in different types of sectors and its scope is on a gradual rise. It is in this context that data science training and courses are being prioritized so that reskilling can happen and the process of automation reaches its pinnacle. Moreover, companies are now looking for thebest data science courses in India so that the existing roles like data scientist, data analyst and data architect can be filled up within the organization by imparting state-of-the-art training to employees. Let us now look at some of the technical methodologies through which data science finds application in various sectors.
1- Detection of anomalies
When we look at the life cycle of data science, we find that the first process in this life cycle is gathering of large data sets. This is followed by the cleansing of data sets and their subsequent processing. Detection of missing values is one of the most important steps in this life cycle. Detection of missing values is extremely important for statistical analysis and maintaining consistencies within the data set. If the missing values are not dealt with at an early stage and anomalies are not detected, this might probably lead to inaccurate results in the end.
One of the examples of anomaly detection is in the domain of financial services. It is highly possible that we may encounter fraudulent spending behaviour which might be an indicator of an anomalous transaction. The challenge is that we need to process thousands of data sets, the volume and variety of which is really vast.
In order to detect the anomalous nature of fraudulent transactions in real time, it is necessary to process large data sets and track changes in various events. This is only possible if the power of data science methodology is leveraged for detection of cyber attacks and securing financial infrastructure by providing an additional layer of security.
2- Identification of patterns
Identification of patterns in very large data sets is one of the fundamental operations where data science is extremely useful. Pattern recognition is extremely helpful in applications like e-commerce where companies need to identify the behaviour of customers, the type of products, their interests, their browsing history and the like. With the help of data science and data analytics, it becomes easier to carry out processes like customer analytics and product recommendation.
The tech giants that are currently commanding the global market have reached this stage only by leveraging the power of data in discovering the purchasing patterns of customers on the basis of specific parameters like region and interest. In addition to this, identification of patterns can directly affect inventory management as well as the marketing strategies of companies in the long run.
The benefits of pattern recognition are not limited to the above use cases. In fact, the domain of pattern identification ranges from stock trading to risk management and speech recognition to computer vision. All sectors and domains that utilize the above technologies directly or indirectly require pattern recognition in different applications.
3- Personalization systems
Personalization systems are intricately related to customer recommendation and product recommendation systems. In order to align the products and services of a company to the needs and interests of the customers, it is extremely important to use the power of personalization systems. The use of personalization systems not only ensures customer engagement but also customer retention in the long run. Traditionally, companies have been employing ways and means to tailor different products and services to the needs of the customers either in a manual way or in a less automated way.
However, this is no longer possible as the catalogue of product and services extends in large numbers. It is in this context that a fully automated system is required that automatically takes care of the personalization of products according to the needs of the client. A personalization system functions in consonance with big data analytics and recommends different products and services to the customers based on his profile in the database. Over a period of time, it is highly possible that recommendation systems achieve a high degree of precision and categorize customers into similar groups depending upon personal interest. This is what we call hyper personalisation in action.
4- Process of Classification
With the help of data science tools, technologies and methodologies, it is possible to classify and categorize data into various subtypes based on the parameters that we already selected. The process of classification is extremely useful for converting unstructured data into structured data sets. This not only makes the process of analysis easier but also allows us to derive actionable insights from it.
With the help of deep learning, artificial neural networks can be employed to analyze very large data sets and carry out classification in an autonomous manner. At a larger level, it is possible to segregate various images, objects and other document types from each other to make analysis easier. At a smaller level, we can also use other classification criteria depending on our requirements and the type of information that we want to work with. The process of classification not only helps in deriving deeper insights from large data sets but also helps in storing data in separate sections so that they can be easily mined as and when required.
5- Sentiment analysis
One of the most important application areas of data science is sentiment analysis as well as behaviour analysis. Both sentiment analysis and behaviour analysis is intricately linked with the classification and categorization of customers into sub categories on the basis of specific parameters. An example of sentiment analysis is the recognition of the interests of customers as well as their positive and negative experience. This not only helps in responding in an immediate manner to retain customers and guarantee customer satisfaction.
Sentiment analysis is highly relevant for application domains like travel and hospitality. On the basis of browsing history and other interactions with the customer, it is possible to determine the destinations and other prospective services that they might be interested in. This information can later be channelised to target them with specific hospitality services and yield lucrative returns.
The bottom line
There is no doubt in the fact that the power of data science tools and methodologies is being harnessed in a number of application domains. However, we need to recognize the fact that not enough manpower is available to cater to the demands of the data market. Hence, we need to re-skill the existing workforce by training them in data science courses. This will allow us to reap the potential of data science to its fullest.