Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyze, the need to investigate is obvious.

Yet, a significant key part to any data science task as often as possible underestimated is the exploratory data analysis

“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” — John W. Tukey

Construct a Relationship with the Data

Photo by Dương Hữu on Unsplash

Before you can model the data and test your hypotheses, you must assemble a relationship with the data.

You can manufacture this relationship by investing time summarizing, plotting, and investigating genuine data from the domain. This methodology of investigation before modeling is called Exploratory Data Analysis.

In investing time with the data up-front you can fabricate an instinct with the data formats, values, and relationships that assist with clarifying observations and modeling results later.

It is called exploratory data analysis since you are investigating your comprehension of the data, assembling an instinct for how the underlying process that created it works, and inciting questions and thoughts that you can use as the reason for your modeling.

The process can be utilized to once-over to verify the data, to distinguish outliers, and come up with specific strategies for taking care of them. In investing time with the data, you can spot corruption in the values that may flag a flaw in the data logging process.

#data-analysis #data-science #exploratory-data-analysis #machine-learning #data-visualization

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How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious. Yet, a significant key part to any data science task as often as possible underestimated is the exploratory data analysis

In this post, you will discover **Exploratory Data Analysis **(EDA), the techniques and tactics that you can use and why you should be performing EDA on your next problem.

“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” — John W. Tukey

Construct a Relationship with the Data

Before you can model the data and test your hypotheses, you must assemble a relationship with the data. You can manufacture this relationship by investing time summarizing, plotting, and investigating genuine data from the domain. This methodology of investigation before modelling is called Exploratory Data Analysis.

In investing time with the data up-front you can fabricate an instinct with the data formats, values, and relationships that assist with clarifying observations and modelling results later.

It is called exploratory data analysis since you are investigating your comprehension of the data, assembling an instinct for how the underlying process that created it works and inciting questions and thoughts that you can use as the reason for your modelling.

The process can be utilized to once-over to verify the data, to distinguish outliers and come up with specific strategies for taking care of them. In investing time with the data, you can spot corruption in the values that may flag a flaw in the data logging process.

#data-analysis #data-science #exploratory-data-analysis #data-visualization #data analytic

How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyze, the need to investigate is obvious.

Yet, a significant key part to any data science task as often as possible underestimated is the exploratory data analysis

“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” — John W. Tukey

Construct a Relationship with the Data

Photo by Dương Hữu on Unsplash

Before you can model the data and test your hypotheses, you must assemble a relationship with the data.

You can manufacture this relationship by investing time summarizing, plotting, and investigating genuine data from the domain. This methodology of investigation before modeling is called Exploratory Data Analysis.

In investing time with the data up-front you can fabricate an instinct with the data formats, values, and relationships that assist with clarifying observations and modeling results later.

It is called exploratory data analysis since you are investigating your comprehension of the data, assembling an instinct for how the underlying process that created it works, and inciting questions and thoughts that you can use as the reason for your modeling.

The process can be utilized to once-over to verify the data, to distinguish outliers, and come up with specific strategies for taking care of them. In investing time with the data, you can spot corruption in the values that may flag a flaw in the data logging process.

#data-analysis #data-science #exploratory-data-analysis #machine-learning #data-visualization