Data science is a  blend of data inference. Data science uses the scientific method, process, algorithms, and systems to extract knowledge. It uses both structured and unstructured data for insights. Data science is a concept to unify statistics, machine learning, and data analysis. In order to clearly understand actual phenomena. It uses techniques and theories drawn from the field of mathematics, statistics, and information science. In this article, you can be able to learn about   Importance of Data science and data science industry trends. 

Importance of  Data science

With over 7 billion devices connects to the internet right now. as 3.5 million terabyte data generated every day. By the end of this year, it may reach to million terabytes of data every day. So there is a need to study new trends in data science online training.

First thing first

Data science is the field that helps business to grow and set them on a track. There is a huge demand in data science today. The US leads data scientists requiring 198.000 data scientists every year. Besides India joins requirement for data scientists across a diverse range of industries.

What   is data science

Data science is the process of slicing through chunks of data. Further processing and analyzing them for a meaningful information. Hence the information can help business get insight and customer experience. From using GPS to reach a nearby destination and generates a ton of data every day by day. This is an example of data science.

Skills required for data science

Data science requires skills in statistics, data science tools, and communication skills. Besides good knowledge of quanta and business acumen are also required. Data scientist puts all these skills to work on data find a pattern, analyze and extract information.   

Is there need of higher graduation for data science?

There is no need for any degree or Ph. D. for data science. It requires about fundamentals of analytics. You  should have an idea about working on analytics tools and understand the basics of data processing.

Categories of analytics

Analytics is classified into three types

Descriptive analytics

In  Descriptive analytics when you work on data set, describe the pieces of information. For example

If you’re analyzing any bank statement of  the previous month. you say that 40% of income spent on shopping, 20% of income spend on traveling, 35% income spent on food and finally 5% income on personal use. This type is called descriptive

Predictive analytics

In a predictive analysis, seems like you can forecast or estimate with historical data. For instance, Using the bank statement  you can predict how your expenses will be

Prescriptive analytics

In prescriptive analytics is when you want to rectify your expenditure .for example you’re spending too much on food and traveling. So by using prescriptive analytics can tell the best category for you to work on to reduce the expenses.

The data scientist will aware of machine learning

Machine learning refers to the development of systems that can learn and adapt. Later improve the data that is fed to them. almost Siri and Google maps are the best examples of machine learning. You can notice clearly google maps come up with optimized and predictive insights on your destination.

Types of Data sciences

In structured data, data can be categorized, segmented and put into the database. Whereas in Unstructured data cannot be categorized. So, unstructured data include share social media posts, books, audio recordings and more. R is the most popular programming language in Importance of Data science.

Data science with IOT

Data science is related to Internet of things. Because Iot is about data generation and data science is about analyzing it. So, On becoming data scientist you will be updating skills enough to be a part of big technology.

More than learning about data science is practicing it. Data is never clean .before you start imaging about saving the company from the loss of millions of dollars. remember you will be spending more time on cleaning data that generates insights from it. Therefore once it was cleaned that you can sit down to perform analytics. you can also get better knowledge on onlineitguru tutorials for clearly understanding of importance of Data science online course concepts.

Recommended Audience :

Developers

Analytics Managers

Business  Analyst

Hadoop professionals

Prerequisites

Its good to have a Basic Knowledge   on  Mathematics, statistics and aptitude . And any one of the programming languages like R. Finally data bases and data sets like SQL and Hadoop were added  advantage.

 
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