What is Data Science?


Data has replaced oil as the new commodity, and every business, regardless of sector, is seeking for innovative methods to handle and store massive amounts of data. Until 2010, most businesses found this a difficult task. 

The goal for each organization was to create a framework or solution that would allow them to store massive amounts of data. Because Hadoop and other platforms have made it simpler for enterprises to store vast amounts of data, they are also focusing on techniques and solutions for processing data. Data science is the only way to do this. 

It's crucial to remember that data science is the way of the future. It's critical to understand what data science is, especially if you want to contribute value to your company. 

Data Science: An Overview 

Data science is a collection of methods, techniques, philosophies, and languages used to uncover hidden patterns within a data set's variables. 

This may prompt you to ask how this differs from the data analysis that has been done for years. The reason is that previously, we could only utilize tools and algorithms to describe the variables in a data set; however, data science makes it simpler to anticipate outcomes. 

A data analyst solely analyses previous data sets to describe what is happening in the present. 

A data scientist, on the other hand, merely looks at the data to see if there are any insights to be gained from it. He also employs complex algorithms to determine the likelihood of an event occurring. He examines the facts from a variety of perspectives. 

Data science is utilized to make educated judgments based on existing data set forecasts. To get this information, you may use a variety of analytics on the data collection. In the next sections, we'll go through these in more detail. 

Predictive Casual Analytics 

Predictive causal analytics is required if you wish to create a model that predicts the possibilities or consequences of a future event. Assume you work for a credit firm and lend money to people depending on their credit scores. 

You'll be concerned about your clients' capacity to pay back the money you've given them. Using payment history, you may create models to do predictive analysis on the data. This might assist you in determining whether or not the consumer will pay you on time.

Prescriptive Analytics 

It's possible that you'll need to employ a model that can make the necessary judgments and adjust the parameters based on the data set or inquiry. 

You'll need to employ prescriptive analytics to do this. This type of analytics is mainly concerned with giving accurate data so that you can make an informed decision. 

This form of analytics may also be used to forecast a variety of related events and actions. 

A self-driving automobile is an example of this sort of analytics. This is something we've looked at before. You may utilize the data obtained from the automobiles to run a variety of algorithms and utilize the findings to make the car smarter. 

This makes it easy for the automobile to make the appropriate judgments when it comes to turning, slowing down, speeding up, or determining which way to go. 

Artificial Intelligence (AI) 

Make forecasts Using unstructured, semi-structured, and structured data sets, you may create predictions using a variety of machine learning methods. Assume you work for a financial institution and have access to transactional data. 

To forecast future transactions, you'll need to create a model. You'll need a supervised machine-learning method to complete this analysis. These methods are used to train the computer with previously collected data. 

You may also design and train a model to detect potential frauds based on previous data using supervised machine learning methods. 

Pattern Recognition 

You won't find variables in every data set that you can utilize to create the appropriate predictions. This isn't correct. Every data collection contains a hidden pattern, which you must discover in order to generate the needed predictions. 

Because there are no pre-defined labels in the data set with which to categorize the variables, you'll need to utilize an unsupervised model. Clustering is one of the most frequent techniques for detecting patterns. 

Assume you work for a telephone firm and are entrusted with determining where towers should be placed in order to construct a network. 

The clustering technique may then be used to determine where towers should be placed to guarantee that every user in the region receives the best signal strength. 

It's critical to grasp the differences between data science and data analytics methodologies, based on the examples above. Only to a limited extent does the latter encompass the use of forecasts and descriptive analytics. Data science, on the other hand, is mainly concerned with the use of machine learning and predictive casual analytics. Now that you know what data science is, let's look at why companies need to employ it in the first place.