Taking an in-depth look at Data Science

Taking an in-depth look at Data Science

Data Science has become a popular topic among professionals as well as organizations, especially the ones whose work involves collecting data and deriving meaningful understandings so that it can help in the growth of the business. A set of relevant data is an asset for any organization; however, it has to be processed effectively. As we have entered the age of big data, the need for storage has grown even more. There are frameworks like Hadoop which can handle the storage part, but the focus has also been on the processing of data. Let us discuss what data science is and its role in business.

What is Data Science?

Data Science can be called a mix of several algorithms, tools, and machine learning principles to discover hidden patterns from the raw data. It is basically used for making decisions as well as predictions by using predictive causal analytics, machine learning, and prescriptive analytics.

Predictive causal analytics: If you are looking for a model that is capable of predicting the chances of specific events in the coming days, you would be required to put on predictive causal analytics. For example, if you are putting funds on credit, the possibility of customers clearing the credit payments in the future on time certainly concerns you. This is when you can create a model which will carry out predictive analytics on the customer’s payment record to foresee if the payments in the future will be made on the expected time.

Prescriptive analytics:  If you are looking for a model that comes with the intellect of deciding on its own and the capability of modifying it with the dynamic factors, you will surely require prescriptive analytics for the same. This is comparatively a new field but is about advising. To put it in other words, it is not limited only to predicting but also comes up with suggestions of several prescribed actions and outcomes associated with it.

Machine learning for making predictions: If you have transactional data of a company that deals in finance, and you need to create a model to understand and even determine the future trend, machine learning algorithms are going to be the best for you. This comes under the paradigm of supervised learning. The reason it is called supervised learning is that you have the data on the basis of which you would be able to train the machines.

Machine learning for pattern discovery: If you do not have the parameters on the basis of which you will be able to make predictions, you need to figure out the hidden patterns in the dataset so that meaningful predictions can be made. This is basically the unsupervised model since you do not have predefined labels for groups. The common algorithm that is used for pattern discovery is Clustering.

Significance of Data Science in Businesses

Businesses have worked with small sets of structured data to large sets of semi-structured data and even unstructured data from several sources. The traditional Business Intelligence tools are not that effective when it comes to processing a huge group of unstructured data. Therefore, Data Science is an option that has more advanced tools for working on large volumes of data that come from various types of sources which include multimedia files, text files, financial logs, sensors and instruments, and marketing firms.

The relevant use-cases that are even the reasons why Data Science is becoming very popular among the organizations are mentioned herein below.

  • Data Science comes with a huge number of applications in predictive analytics. In weather forecasting, data is collected from radars, satellites, ships, and even aircraft to create build models that help in forecasting weather and helps in accurately predicting natural calamities that may happen in the future. This ultimately helps in taking the right measures and avoiding damage as much as possible.
  • Product recommendations have become more detailed with traditional models that draw understanding from purchase history browsing history and demographic factors at a basic level. With the help of Data Science, huge volumes along with various data can train models effectively so that accurate recommendations can be shown.
  • Data Science also helps in effective decision making. An apt example of this is a self-driving or intelligent car. An intelligent car collects data from its surroundings with the help of several sensors, including cameras, radars, and even lasers, to come up with a visual of the surroundings. On the basis of the data and even advanced Machine Learning algorithms, it comes with important driving decisions such as stopping, turning, speeding, and many more.

Who is a Data Scientist?

A Data Scientist is one who practices Data Science. A Data Scientist receives information from the scientific fields as well as applications, from mathematics to statistics and even computer science. They crack difficult data problems with strong proficiency in some of the scientific disciplines. The qualities of a data scientist are Statistical Thinking, Multi-modal communication skills, Technical Acumen, Curious mind, and Creativity. 

If you want to build a career in data science, you can pursue a course on the subject from Great Learning. There are courses for candidates who have experience of less than and more than three years. Depending upon your years of experience, you can choose a course and shape your career.

Hope this article helps you a lot for understanding this topic. If you’re interested in free online courses with certificates, So enroll today on Great Learning Programme.


IITSWEB is the Chief Business Development Officer at IITSWEB, a Magento design and development company headquartered in Redwood City, California. He is a Member of the Magento Association and an Adobe Sales Accredited Magento Commerce professional. Jan is responsible for developing and leading the sales and digital marketing strategies of the company. He is passionate about ecommerce and Magento in particular — throughout the years his articles have been featured on Retail Dive, Hacker Noon, Chief Marketer, Mobile Marketer, TMCnet, and many others.

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