# Introduction to OpenCV

OpenCV is a python library, that is very useful in Visualization and Analyzing purposes.

“CV basically stands for Computer Vision which is used in machine learning, and image processing as well as it plays a major role in real-time operation which is very important in today’s systems”.

By using it, one can process images and videos to identify objects, faces, or even handwriting of a human.

# A Brief History of OpenCV

OpenCV was initially an Intel research initiative to advise CPU-intensive applications. It was officially launched in 1999.

# Statistical Modelling In Python

Hey Everyone,

As in my last article, i have explained the Time Series Modelling in Python.

Let’s have a look on to the another important modelling in Python i.e, Statistical Modelling.

# Statistical Modelling — An Overview

Statistical modelling gives you the ability to asses, understand and make predictions about data.

Statistical modelling is an important part of risk analysis and safety in various engineering areas (mechanical engineering, nuclear engineering), in the management of natural hazards, in quality control, and in finance.

In this Blog, I’ll be Explaining the 2 important topics of Statistical modelling, i.e Linear Regression and ANOVA using Stats model.

First of all…

# What is a Time Series?

Time series is a sequence of observations recorded at regular time intervals.

Depending on the frequency of observations, a time series may typically be hourly, daily, weekly, monthly, quarterly and annual. Sometimes, you might have seconds and minute-wise time series as well, like, number of clicks and user visits every minute etc.

Need of Time Series:

Time Series is needed as it is the preparatory step before you develop a forecast of the series.

Besides, time series forecasting has enormous commercial significance because stuff that is important to a business like demand and sales, number of visitors to a website…

# Hello Everyone!!

Let’s have a quick glance on the Categorical plots in python. This blog will explain you about What are Categorical plots and The various types of Categorical plots that are present in Python.

Let’s get started!!

# What are Categorical Plots???

As we all know that Python provides various plotting techniques and various libraries , one such library is Seaborn.

Seaborn besides being a statistical plotting library also provides some default datasets. We will be using one such default dataset called ‘iris’.

Categorical Plots are the plots that consists of more than one category of data.

# Geographical Mapping to Visualize Covid-19 Cases in India

The extraction of actionable insights from raw data is what we call as Data Science. It is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.

Coronavirus or Covid-19 needs no introduction. It has already been declared as a pandemic by WHO and in past couple of weeks it’s impact has been deleterious. In this article i am going to tell, how one can visualize the Covid-19 data set using geographical plots and can track Covid-19 spread in India using python.

AIM: To plot the states having…

# What is Seaborn??

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

# COVID-19 Data Analysis using Data Science in Python

As we know that Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus2(SARS-CoV-2).

This pandemic has caused global social and economic disruption, including the largest global recession since the Great Depression.

In this article, we are going to analyse the Covid-19 data using Python and some graphing libraries, you can project the total number of confirmed cases of COVID-19, and also display the total number of deaths for a country (this article uses India as an example) on a given date. …

# Why Learn Data Cleaning?

Data scientists can end up doing a wide variety of things across a wide variety of industries, but almost every data science job shares at least one thing in common: data cleaning.

When some part of our data is missing, due to whichever reason, the accuracy of our predictions plummets.Hence, in such a case Data Cleansing comes into picture. With the help of Data Cleansing one can get the accurate results.

According to IBM Data Analytics you can expect to spend up to 80% of your time cleaning data.

# Introduction To Pandas:

Before understanding the Data Frames in pandas, let’s have a brief discussion on :

What are Pandas??