Python has very rich visualization libraries. I wrote about the visualization in Pandas and Matplotlib before. Mostly they were the basics with a touch of some advanced techniques. This is another visualization tutorial.

I decided to write a few articles on some advanced visualization techniques. This is the first one of them. In this article, I won’t work on any basic visualization. All the visualizations in this article will be some advanced visualization techniques. Some are not so advanced but this will not focus on any basic visualization. …

The Neural Network has been developed to mimic a human brain. Though we are not there yet, neural networks are very efficient in machine learning. It was popular in the 1980s and 1990s. Recently it has become more popular. Probably because computers are fast enough to run a large neural network in a reasonable time. In this article, I will discuss how to develop a neural network algorithm from scratch in python.

**I recommend that please read this ‘Ideas of Neural Network’ portion carefully. But if it is not too clear to you, do not worry. Move on to the implementation part. I broke it down into smaller pieces there. …**

Time series data is very important in so many different industries. It is especially important in research, financial industries, pharmaceuticals, social media, web services, and many more. Analysis of time series data is also becoming more and more essential. What is better than some good visualizations in the analysis. Any type of data analysis is not complete without some visuals. Because one good plot can provide you with a better understanding than a 20-page report. So, this article is all about time-series data visualization.

I will start with some very simple visualization and slowly will move to some advanced visualization techniques and…

Gaussian distribution is the most important probability distribution in statistics and it is also important in machine learning. Because a lot of natural phenomena such as the height of a population, blood pressure, shoe size, education measures like exam performances, and many more important aspects of nature tend to follow a Gaussian distribution.

I am sure, you heard this term and also know it to some extent. If not, do not worry. This article will explain it clearly. I found some amazing visuals in Professor Andrew Ng’s machine learning course in Coursera. …

It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly.

Time series data is used in:

Banks and Financial institutes

Stock market

Social Media

Power, gas, and oil industries

Periodic measures in a mechanical or chemical process

And many more…

In time-series data analysis, generating dates could be necessary on many occasions in real life. Sometimes we have data but time is not recorded, sometimes we may have to use one countries data for another country’s research or last year’s data this year. The holidays will be different this year than last or this country than another country. This article shows:

a. how to use the in-built holiday calendar.

b. generate a custom holiday calendar.

c. incorporate a series of dates in a dataset.

- Generate a time series that considers all the holidays.

Pandas already have a US holiday calendar built in it. Use the ‘CustomBusinessDay’ function to generate a custom frequency, pass the built-in US holiday calendar. Use this custom business day as the frequency. …

Descriptive statistics summarize, show, and analyze the data and make it more understandable. If the dataset is large, it is hard to make any sense from the raw data. Using descriptive statistics techniques, data can become more clear, patterns might emerge and some conclusions might be evident.

But descriptive statistics do not allow us to reach any conclusion beyond that analysis part. It does not confirm any hypothesis that we have made. You need to study inferential statistics for that. I have added a few links to study inferential statistics at the end of this page.

There are a few general types of statistical measures to describe the…

Graph form data is present in many popular and widely used applications. Web crawlers, computer networks, relational databases, and social networks are some good examples. The graph search algorithms are important for any section of computer science. Also, it is important and useful for many coding interviews.

There are a couple of different graph search algorithms available. This is one of the simplest algorithms for graph search and also a type of prototype for many other graph algorithms. Today I will explain the Breadth-first search algorithm in detail and also show a use case of the Breadth-first search algorithm. …

Numpy is an open-source Python library. This library is essential for data scientists who use python. Some other essential libraries like Pandas, Scipy are built on the Numpy library. So I decided to make a cheat sheet. Here I included all the Numpy functions that I used so far. And I believe these functions will be enough for you to do your job in your everyday work life as a data scientist or a data analyst.

I will start with the very basic Numpy functions slowly moved towards the more advanced ones. But using Numpy is easy. …

What is a depth-first search?

This is one of the widely used and very popular graph search algorithms. To understand this algorithm, think of a maze. What we do when have to solve a maze? We take a route, keep going till we find a dead end. After hitting the dead end, we take a backtrack and keep coming until we see a path we did not try before. Take that new route. Again keep going till we find a dead end. Take a backtrack again….

The depth-first search works almost in the same way. Using this type of backtracking process. From the starting point, it travels until it finds no more paths to follow. Then takes a backtrack and comes back to a point that has unexplored paths. …

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