Exploratory data analysis(EDA) is a broad topic. It is very hard to cover this in one article. Exploratory data analysis can be done for learning about the data and the relationship between the different features of a dataset. Or it can be done to learn about the features to choose the important features and prepare the dataset so that a statistical model can be fit in it. Whatever the reason is, it is important to learn about the features and the relationship amongst the features. In this article, I will focus on exploratory data analysis(EDA) for data modeling. Though we are not going to do prediction here. We will just focus on the EDA part.
I am using the Boston dataset that is already there in the scikit-learn library. It contains information about the housing price in Boston. First, import the necessary packages and the dataset.
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline from sklearn.datasets import load_boston boston_data = load_boston()
For information, the Boston dataset is organized into two parts. One is the data part which contains features and the other one contains the price of the houses. We are going to include the prices in the same dataset as the features.
df = pd.DataFrame(data=boston_data.data, columns=boston_data.feature_names) df["prices"] = boston_data.target
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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.
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EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.
EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.
There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.
Let’s understand the different sections in the user interface :
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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 –
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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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
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