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Exploring what is Data Analytics and Data Science – Their Importance

Importance of Data Science

In the words of Jeff Weiner, “Data really powers everything that we do.”
Let’s understand how data is crucial to every industry and the way it’s revolutionizing the modern world. Data these days has become an important source of crucial research and analysis. All the data-related technologies are the most sort after fields of today. And here in this article, we will answer your question on what is Data Analytics? Why Data Analytics is Important? What is Data Science? Importance of Data Science, tools in data analytics, types of data analytics, and a lot more.

Experts suggest that in the last 2-3 years, 90% of the global data was generated, to know dive deeper,  check this link. So, if you think deeper, you can understand how such an excellent amount of data is getting generated each year. And this is creating lots of scope for new data experts like data analysts, data engineers, data scientists, etc.

So let’s dive into the world of data analytics and data science.

Highlighting More on What is Data Analytics

Data Analytics collaborates theory with practice to analyze and communicate data-related suggestions that help executives and managerial heads in crucial decision-making. Data analytics is fundamental to data science. Data Analytics works on existing datasets by performing statistical research and analysis. With the research and analysis of data experts, we are busy investigating the unanswered problems.
Some of the important tools in data analytics are Python, Microsoft Excel, SQL, Tableau, Power Bi, Rapid Minor, Apache Spark, QlikView, R, and more. The experts in these fields are majorly from computer science or I.T. background.

Best Insights on Why Data Analytics is Important

Businesses these days make a good use of data analytics to enhance their operations and performance. Proper execution of data analytic practices into routine business activities can help incorporate efficient ways of working. Data analytics can answer everything be it customer behavior, choices, trends, buying attitude, and more.

What is Data Science?

Data science works on huge datasets using innovative tools and technologies to gather valuable inputs and make smooth decision-making. Seeing the importance of this field, it is one of the most discussed areas of Information Technology. Knowing Machine learning; modeling, programming, statistics, and databases are obligatory for understanding data science.

Importance of Data Science

1.Data science has huge importance in every industry be it I.T., marketing, healthcare, retail, construction, banking, finance, education, and lots more.
2.Data science makes crucial decision-making a cakewalk for decision-makers.
3.It helps find solutions to very complicated problems.
4.The banking and finance sector utilizes data science to detect frauds and scammers and deal with them.
5.The retail sector is utilizing data science to provide exciting experiences to the end-user and retain their trust in the product and services for a longer time.
6.Data science is valuable in creating advanced construction models efficiently and effectively at a reasonable cost.

What do data analysts do?

Now, let’s talk about the role of data analysts. Their role is mostly to work on stable data and get detailed inferential analysis. One of their main roles is to test and reject models and hypothesis. They work on finding the trends and figures. They put this information for the benefit of the business system.

Explore the types of Data Analytics

Data analytics has four important types that we will explain here. These are Descriptive analytics, prescriptive analytics, predictive analytics, and diagnostic analytics.
Types of Data Analytics
● Descriptive Analytics is about finding past actions. Like, yearly website traffic, monthly earnings, quarterly sales, etc. These analytics become useful in getting the trends of the business.
● Prescriptive Analytics deals with finding the required business actions. It is more of coming up with a solution for a potential threat to the business. And doing this, you often need to use complex algorithms along with Machine Learning.
● Predictive Analytics as the name suggests it anticipates future outcomes by finding forms and patterns in descriptive as well as diagnostic analyses. It can help in taking early action.
● Diagnostic Analytics helps in finding the reasons behind some outcomes by distinguishing between the detailed data sets to check the patterns and dependencies. It further helps in knowing the cause of the positive or negative result.

Let’s dive deeper to understand the role of a data analyst

The question ‘what does a data analyst do?’ depends on the organization where one is employed. It depends to the level an organization has matured itself in using data-driven technologies. A general idea of the responsibilities of a data analyst is presented below:
Data Mining from its primary and secondary levels and later organizing it in a format that it can be read by humans or machines.
● Maintaining as well as designing data systems as well as datasets; covers finding solutions to errors in coding as well as different data-based problems.
● Presenting their work based on trends at a local, regional, national or global level that would affect both their organization as well as the industry.
● The utilization of statistical tools to analyze data sets, giving focused attention to patterns and trends which could be useful for either diagnostic or predictive analytics or both.
● They also prepare reports for leadership to understand and present the trends, predictions as well as patterns using the relevant set of data.
● They have a collaborative role to play with the engineers, programmers, as well as organization heads to analyze the scope of process enhancement, suggest system modifications, as well as the development of policies for the governance of data.

Quick Facts about Data:

● Data scientists spend their 80% of the time managing data.
● Among professional developers, Python and SQL are now the third and fourth important technologies.
● An AI-assisted text prediction model was skilled to write the novel Harry Potter.

Thus, tried to elaborate some important aspects related to data Analytics what a data analyst does, what is data science? Importance of data science and data analytics. There is still a lot to discuss and talk about data that will be covered in the upcoming blogs.

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