Using HuBMAP dataset hosted on Kaggle
Exploratory Data Analysis is one of the best practices used in data science today.Basically it is essentially a type of storytelling for statisticians.We can say EDA as “A first look at the data”. Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually.The main goal of exploratory data analysis is to obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.This step is very important especially when we arrive at modelling the data in order to apply Machine Learning.Exploratory Data Analysis is nothing but the complete film of the data. When we watch the characters in film, we get to understand the relations between them. How to link the characters to the story of film so that it will never go off track is very important to imagine for a writer. That curious imagination is required for the data engineer to explore the data-set. In a hurry to get to the machine learning stage, some data scientists either entirely skip the exploratory process or do a very perfunctory job. This is a mistake with many implications, including generating inaccurate models, generating accurate models but on the wrong data, not creating the right types of variables in data preparation, and using resources inefficiently because of realizing only after generating models that perhaps the data is skewed, or has outliers, or has too many missing values, or finding that some values are inconsistent.So, one of the most important components to any data science experiment that doesn’t get as much importance as it should is Exploratory Data Analysis (EDA). Hence it is a critical step in analyzing the data from an experiment.
#python #data-analysis #programming #developer
Exploratory Data Analysis (EDA) is a very common and important practice followed by all data scientists. It is the process of looking at tables and tables of data from different angles in order to understand it fully. Gaining a good understanding of data helps us to clean and summarize it, which then brings out the insights and trends which were otherwise unclear.
EDA has no hard-core set of rules which are to be followed like in ‘data analysis’, for example. People who are new to the field always tend to confuse between the two terms, which are mostly similar but different in their purpose. Unlike EDA, data analysis is more inclined towards the implementation of probabilities and statistical methods to reveal facts and relationships among different variants.
Coming back, there is no right or wrong way to perform EDA. It varies from person to person however, there are some major guidelines commonly followed which are listed below.
We will look at how some of these are implemented using a very famous ‘Home Credit Default Risk’ dataset available on Kaggle here. The data contains information about the loan applicant at the time of applying for the loan. It contains two types of scenarios:
on at least one of the first Y instalments of the loan in our sample,
We’ll be only working on the application data files for the sake of this article.
#data science #data analysis #data analysis in python #exploratory data analysis in python
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 (EDA) is one of the most important aspect in every data science or data analysis problem. It provides us greater understanding on our data and can possibly unravel hidden insights that aren’t that obvious to us. The first article I’ve wrote on Medium is also on performing EDA in R, you can check it out here. This post will focus more on graphical EDA in Python using matplotlib, regression line and even motion chart!
The dataset we are using for this article can be obtained from Gapminder, and drilling down into _Population, Gender Equality in Education _and Income.
The _Population _data contains yearly data regarding the estimated resident population, grouped by countries around the world between 1800 and 2018.
The Gender Equality in Education data contains yearly data between 1970 and 2015 on the ratio between female to male in schools, among 25 to 34 years old which includes primary, secondary and tertiary education across different countries
The _Income _data contains yearly data of income per person adjusted for differences in purchasing power (in international dollars) across different countries around the world, for the period between 1800 and 2018.
Let’s first plot the population data over time, and focus mainly on the three countries Singapore, United States and China. We will use
matplotlib library to plot 3 different line charts on the same figure.
import pandas as pd import matplotlib.pylab as plt %matplotlib inline ## read in data population = pd.read_csv('./population.csv') ## plot for the 3 countries plt.plot(population.Year,population.Singapore,label="Singapore") plt.plot(population.Year,population.China,label="China") plt.plot(population.Year,population["United States"],label="United States") ## add legends, labels and title plt.legend(loc='best') plt.xlabel('Year') plt.ylabel('Population') plt.title('Population Growth over time') plt.show()
#exploratory-data-analysis #data-analysis #data-science #data-visualization #python
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 :
#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis
Exploratory data analysis is one of the best practices used in data science today. While starting a career in Data Science, people generally don’t know the difference between Data analysis and exploratory data analysis. There is not a very big difference between the two, but both have different purposes.
Exploratory Data Analysis(EDA): Exploratory data analysis is a complement to inferential statistics, which tends to be fairly rigid with rules and formulas. At an advanced level, EDA involves looking at and describing the data set from different angles and then summarizing it.
Data Analysis: Data Analysis is the statistics and probability to figure out trends in the data set. It is used to show historical data by using some analytics tools. It helps in drilling down the information, to transform metrics, facts, and figures into initiatives for improvement.
We will explore a Data set and perform the exploratory data analysis. The major topics to be covered are below:
— Handle Missing value
— Removing duplicates
— Outlier Treatment
— Normalizing and Scaling( Numerical Variables)
— Encoding Categorical variables( Dummy Variables)
— Bivariate Analysis
#data-analysis #statistics #exploratory-data-analysis #data-science #python