Overview of Exploratory Data Analysis With Haberman Dataset

“Data will talk, if you are willing to listen”- Jim Bergeson

With the proper use of data, one can gain insights and use it for numerous purposes. Raw data has no story to tell. So, to understand and gain insights from data, after the data collection process, exploratory data analysis comes into the picture. It is a crucial process to recognize patterns and understand data to prepare the model.

This article is divided into the following sections:

1. Overview of Data Exploratory Analysis (EDA)
2. EDA for Haberman’s dataset

Overview of Data Exploratory Analysis (EDA):

What is EDA?

The process to explore and understand data to gain insights from the data. It can be context as “Look at the first sight” for solving any Data Science problem. It takes a step closer to the goal of solving the problem at hand.

Why apply EDA?

To summarize important features, to recognize patterns & distribution curves, to detect outliers and anomalies, to find a number of classes, or distribution of data/classes, to test underlying assumptions, etc by analyzing and visualizing. Basically to know what the data is trying to say!

EDA process

1. Question
2. Verify
3. Write
4. Repeat

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

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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

Overview of Exploratory Data Analysis With Haberman Dataset

“Data will talk, if you are willing to listen”- Jim Bergeson

With the proper use of data, one can gain insights and use it for numerous purposes. Raw data has no story to tell. So, to understand and gain insights from data, after the data collection process, exploratory data analysis comes into the picture. It is a crucial process to recognize patterns and understand data to prepare the model.

This article is divided into the following sections:

1. Overview of Data Exploratory Analysis (EDA)
2. EDA for Haberman’s dataset

Overview of Data Exploratory Analysis (EDA):

What is EDA?

The process to explore and understand data to gain insights from the data. It can be context as “Look at the first sight” for solving any Data Science problem. It takes a step closer to the goal of solving the problem at hand.

Why apply EDA?

To summarize important features, to recognize patterns & distribution curves, to detect outliers and anomalies, to find a number of classes, or distribution of data/classes, to test underlying assumptions, etc by analyzing and visualizing. Basically to know what the data is trying to say!

EDA process

1. Question
2. Verify
3. Write
4. Repeat

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

Overview of Exploratory Data Analysis With Haberman Dataset

“Data will talk, if you are willing to listen”- Jim Bergeson

With the proper use of data, one can gain insights and use it for numerous purposes. Raw data has no story to tell. So, to understand and gain insights from data, after the data collection process, exploratory data analysis comes into the picture. It is a crucial process to recognize patterns and understand data to prepare the model.

This article is divided into the following sections:

1. Overview of Data Exploratory Analysis (EDA)
2. EDA for Haberman’s dataset

Overview of Data Exploratory Analysis (EDA):

What is EDA?

The process to explore and understand data to gain insights from the data. It can be context as “Look at the first sight” for solving any Data Science problem. It takes a step closer to the goal of solving the problem at hand.

Why apply EDA?

To summarize important features, to recognize patterns & distribution curves, to detect outliers and anomalies, to find a number of classes, or distribution of data/classes, to test underlying assumptions, etc by analyzing and visualizing. Basically to know what the data is trying to say!

EDA process

1. Question
2. Verify
3. Write
4. Repeat

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

Exploratory Data Analysis in Few Seconds

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.

About Pandas Visual Analysis

1. It is an open-source python library used for Exploratory Data Analysis.
2. It creates an interactive user interface to visualize datasets in Jupyter Notebook.
3. Visualizations created can be downloaded as images from the interface itself.
4. It has a selection type that will help to visualize patterns with and without outliers.

Implementation

1. Installation
2. 2. Importing Dataset
3. 3. EDA using Pandas Visual Analysis

Understanding Output

Let’s understand the different sections in the user interface :

1. Statistical Analysis: This section will show the statistical properties like Mean, Median, Mode, and Quantiles of all numerical features.
2. Scatter Plot-It shows the Distribution between 2 different features with the help of a scatter plot. you can choose features to be plotted on the X and Y axis from the dropdown.
3. Histogram-It shows the distribution between 2 Different features with the help of a Histogram.

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

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