Why Data Science is important?

In today’s world companies and businesses are becoming data-driven. Because they have realized the importance of processing of data. Data Science is one of the top trending technology of the era and data science has stretched its roots deep down in the corporate industry. Due to this vast inference, new age learners and working professionals are keen to learn this technology. To curb this, various online data science course are available through which one can master data science and begin career as a data scientist.

What does data science means?

In simple words if I have to explain, it is about analyzing and exploiting the huge amount of data and to use it for the better performance of the businesses. Businesses nowadays are using Data science as a tool for tackling huge amount of data based on that they are taking crusial decisions about the future.

Now the question arises from where this much amount of data is being created. So, let me explain this by small example like mobile and various digital platforms have typically resulted in such huge data.Data science  is finding its application in every field today mainly in business, medicine, telecommunication, transportation etc. Business strategies are now majorly dependent on  data science.

Major steps involved in understanding of DATA SCIENCE is explained in this figure

BUSINESS UNDERSTANDING

Having an understanding of the variables in the business is very important in this step.

Like in this step we have to always question ‘why’ this in the process.

Mainly the objectives of the project should be made clear here.

For example if I take the examples of a Cancer treatment hospital.

Then here the question that need to be asked and noted are what are major causes of cancer,catagories of cancer, major cells affected, cost estimated for treatment.

DATA MINING

This step majorly involves in collecting data from the objectives defined. Finding the right data is skill to be gained.  And that’s what is required here. 

For example various vision sensors are used to collect data in health care industries, suppose cancer patients are being under vision and various data is collected. Treatment cost is also noted.

DATA CLEANING

This is the most time consuming step as further analysis of the data will be based on this step. 

For example in treatment of cancer the data collected from the patients are catogarised., any duplicacy in data is being removed etc.

DATA EXPLORATION

Here, is where analysis part starts. Here the data scientists use various tools to analyse the data. For example making use of plots, graphs, stats etc.

Various parameters like behavior of humans in cancer are labeled as variables and analysed. Understanding the pattern of the disease is important.

FEATURE ENGINEERING

Here we make use of the results we find from the data. We try to remove the data or content which is not desirable. Like noise from the signal, or removing unrequired pixels.

For example various algorithms are being used for treatment of cancer.

PREDICTIVE MODELING

In this particular step we make use of nueral network and machine learning technologies to make the data learn and adapt the process.

DATA VISUALIZATION

After analyzing and working on the data its is important to present to others such that it is meaningful. And understood by the people. 

I can personally say there are many good books for visualizing data.