Data science is the field that merges programming, math, statistics and domain expertise to procure insights from data. With the help of machine learning algorithms, data scientists produce artificial intelligence (AI) by using text, numbers, audio, video and images. These systems replace and perform, in a very efficient manner, tasks that were earlier performed and required human intelligence. They also provide patterns in data that allow businesses to translate into higher profits in their work space. A data scientist connects the dots between data and business.
Data science encapsulates statistical readiness, programming skills and visualization techniques coupled with business sense. It is important to be eager and willing as many business questions are required to be turned into presentable answers through data.
Why is data science necessary?
Data available earlier was highly structured and organized. It could have been analyzed by the simple business intelligence (BI) tools, which are nowadays not complex enough to analyze and handle the volume of data post the advent of the internet. By using algorithms and other analytical tools, meaningful patterns are found from the data.
Take for example, if you would like to provide better customer experience by personalizing the experience for a customer. This is done by utilizing and analyzing the past data and assessment of behavior of that particular customer. The patterns of these behaviors are looked at by algorithms and are used for providing this experience. Based on their preferences, products are recommended to do so. Weather forecasting, smart cars, online shopping platforms, all uses data science to improve the Customer Relationship Management (CRM).
CRM is primarily used to maximize a company’s profit with the help of predictive analysis. When coupled with predictive analysis, there are higher rates of accuracy and that is a key point of bringing in a customer. Due to specific targeting based on previous data, they tend to be rather correct in predicting the pattern of a user. They also help in the collection of data from users in a more efficient manner. MNC’s like Amazon, Google, Netflix and YouTube have used CRM and predictive analysis to personalize their sites and products based on the previous uses so as to suit a customer while aiding them to easily find what they might like and increasing profits through advertising. They have done so by using complex algorithms that search for patterns based on the assigned parameters.
Three major techniques used in predictive analysis are sequencing, lack of action, cross selling.
- Sequencing is calculating the probability of an event’s likely occurrence if two activities have preceded a final one. This is done based on historical data after observing similar behavior over a period of time.
- Lack of action is a pattern displayed by a customer when they might be planning to stop using a service provided by a company. Similar behavior can be recognized from past data displayed by customers who might have left.
- Cross selling is a strategy wherein a user is suggested similar products that might have been sold when other customers had made similar product purchases. Cross selling is most effective on existing customers.
Data science is what is deciding where a company may stand, and they are becoming essentials. So, look up the best data science training in bangalore for learning the profession and get rocking.
360DigiTMG – Data Science, Data Scientist Course Training in Bangalore
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