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Post By Admin Last Updated At 2020-06-11
What is Data Science?

Everybody is moving into the world of Data, the requirement for it is growing Day by Day. It is a challenging task for IT companies until 2009. After that Hadoop and few other frameworks have solved the errors for storing and Processing Data. Data science is the Superman here. Each and every scene, which we are watching in Movies like, Mission Impossible, Avengers are Made by Data science. Future of Artificial Intelligence is in the hands of Data science. However, you should know why Data science is Important, how it solves problems and add Profit to your Business. Before that we need to know what is data science?

In this Blog, I will clearly explain you about the following topics

1) Is Data Science has a life cycle, in which way it will help you!

2) How Data science Differ from Data analytics and Business Intelligence.

3) What is the meaning of Data Science?

4) The Requirement of Data science in today world.

By the completion of this Blog, you will know what is Data Science. How data science implemented to get, the best results in any company. For complete information on Data science, you can directly join Data science online training by OnlineITguru with 24/7 and get complete lifetime access.

Is Data Science has a life cycle, in which way it will help you!

Here you can see stages of Data science.

what is data science

Acquisition of Data, Tracking the Data and Data analytics from, where it is coming and when it is coming. Whether this Data need an update or not.

This whole process is Important and should be done in the complete life cycle, of data science Project.

Data Preparation, Data scientists have to Re-frame the Data in a manual method, by writing a program in languages like R and Python. After that Data is converted like an example, CSV or JSON file formats.

Accordingly, Data Mining is the way of discovering Data. Extracting, transforming, and storing the data. These all comes under data mining.

Data Modelling. It is the term explains about software engineering. Designing a Data Model from the database, by utilizing some formal methods.

Model maintenance, when data stored in a data server, it needs some maintenance. As a rule for this, we have to implement machine learning concepts. For the most part, You can master above concepts by knowing what is Data analytics.

How Data science Differ from Data analytics and Business Intelligence.

Data science is for the future. It can analyze past, present, and future Data, the goal is to create Decisions that to be informed. It just like question bank, give an answer for when, where and how the Programs happening.

Generally Business Intelligence, it basically works with old Data. Offer Results to show the latest business trends. With BI you can take Data from Internal and external sources. You can design your own dashboards to solve the questions like. Financial year end and Business issues. In the first place, BI can show some Impact in the upcoming future.

What is the meaning of Data Science?

Generally, Data Science is a combination of algorithms, machines learning techniques. Main target is to get unknown Designs from Raw Data.


Machine learning for unknown Design Discovery- for this method we need clustering. In the meantime, if you want to find a road map for establishing traffic signals in your city. You can use clustering for this method.

Generally Single Method Analytics With this type of analytics, they will get predicted ways and suggestions to complete the task.

Example, how Tesla cars using Auto Pilot for its model X.

In summary Predictive Analytics- here we can predict the future outcome of our present input. Let us consider, when giving a bank loan, we will see their old transactions and cibil score.

The Requirement of Data science in today world.

Incidentally, in the classic view, we get Data in Structure format and small in number. You can analyze, this data by small BI tools. In old systems, we get data in a structured format. Today we receive Data, in the unstructured way or semi-structured way.

Especially the above graph shows the statistics for data science. In this case, Data can be generated from different sources, like financial, sensors, multimedia. Small BI tools not capable of processing, Big Volume and variety of Data. Therefore, we need many critical analytical tools and algorithms for getting Trustworthy Insights.


Consequently, this is the reason, why Data science is becoming popular every year. I think you have satisfied by reading our blog. We are here to help you and guide to become masters in data science. Feel Free to contact our Data Scientists.