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Python for Data Science – Data Visualization on Standard Datasets

In this note, we will use all the famous libraries such as Matplotlib and Seaborn for data visualization, Pandas for table-wise data manipulation, Numpy for standard numerical operations, and Sci-Kit learn for machine learning algorithms. We will work on few standard datasets that are available in the seaborn package, and these are as follows: Flights …

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Elastic Search – Introduction

Elasticsearch is a full-text search and analytics engine based on the Apache Lucene search engine library. It is a distributed, highly available, and scalable search engine solution that provides a wide range of algorithms to be configured for indexing and search of text data. It is an open-source solution developed in java and can be …

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Elastic Search – ELK Stack Architecture

Various software and components are available in the market to manage and extract meaningful information from the massive data. These systems work as follows: data collection, pre-processing, aggregation, storage, search & indexing, and data analytics.  In this note, we will understand ELK stack architecture. ELK Stack Architecture The classic Elastic Logstash Kibana (ELK) stack Architecture …

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Python Fundamentals – Introduction

This entire note series will learn how to build the skill set to solve real-life problems. However, before that, we will gain knowledge of the python programming language and its key concepts. We will cover how to represent knowledge with data structures, computational metaphors such as iteration and recursion, abstraction of procedures and data types, …

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Descriptive Statistics – Measures of Central Tendency of Data

In the earlier note, we have seen how to create different types of graphics to extract valuable information from a variable (univariate analysis using plots and charts). Moreover, in this note, again, we will learn (univariate analysis using analytical tools) how to use analytical tools to extract the information about the central tendency of the …

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Descriptive Statistics – Graphic and Plots

In the previous note on Descriptive Statistics – Frequency Distribution, we have seen the different aspects of frequency distribution such as absolute frequency distribution, relative frequency distribution, and cumulative frequency distribution. The frequency distribution is one way to make the data compatible to be exposed to the graphical and analytical tools. On this note, we will try to …

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Descriptive Statistics – Variables and Types of Data

In this note, we will get familiarized with different data types primarily used in descriptive statistics. These are the foundation for understanding and selecting graphical and analytical tools for descriptive statistics. The motivation to read the different data types is that we can’t apply all the available measures, such as measures of central tendency, measures of …

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Python Seaborn for Data Visualization – Distribution plots

Distribution plots are used for Univariate analysis. They display a single continuous feature, which helps us learn parameters such as mean, variance, and standard deviations. There are mainly three distribution plots, Rug plot, Histogram, and Kernal Density Estimation (KDE) plot. This note will see all these types of plots and perform hands-on on the dataset called …

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