Deep learning is a subset of machine learning and machine learning is a subset of artificial intelligence (AI). All are within the field called data science.

The next time you are presented with a dataset, remember the power of data visualization and the clarity it can bring to your insights.

Clean data is the lifeline of credible analytics. Clean data is not just beneficial - it is critical.

From analyzing data to making predictions, statistics provides the framework that data scientists use to make sense of vast amounts of information.

Welcome to the fascinating world of Generative Artificial Intelligence (AI)! This post is designed to break down the basics of generative AI in a way that is easy to understand.

Most inferential problems can be formulated as an inference about one or more parameters of a population. In this blog post, I explain two types of statistical inferencing. Happy learning!

I am happy to share in simple steps, how to get into the Data Science field. Many programming languages can be used but here the focus is on Python.

In this blog, I discuss some ways of getting descriptive statistics (mean, median, mode, range, percentiles, interquartile range, variance and standard deviation) of a made-up data.

In this blog I discussed some data types - qualitative, quantitative, discrete and continuous variables.

In this blog post, I explain The Role of Statistics in Data Science - Descriptive vs. Inferential Statistics: Understanding the Difference.

This post outlines data pre-processing of categorical variables using Label encoding, Mapping and One Hot Encoding.

In this post, I explained how to fetch dataset from in-built library - sklearn and perform simple preprocessing (merging and columns rename) for your data science project.

This sample code shows how to create a DataFrame dataset from a Dictionary, convert it to a Dictionary in order to perform some operations and then convert it back to a DataFrame.

In this post, I shared easy ways to import the following file formats for use in Python environment for analysis; .csv, .xlsx, .dat, .data, .parquet, .json, html, sqlite, .txt.

Methods such as forward fill, backward fill, cumulative & reverse cumulative sum were used in this post.

This post shows a way to aggregate data in a very flexible way. All you need is to modify to your use-case. Feel free to like and leave comment below. Good luck!

Here, I compiled common Git commands I have used in the course of my development journey. Use, like and leave comments.

This post describes how to use Git and GitHub to work and connect remotely to your projects. Feel free to use, like and leave comments.

Pascal's triangle, in algebra, is a triangular arrangement of numbers that gives the coefficients in the expansion of any binomial expression, such as (x + y)^n.

This simple code shows how to use Logistic regression for multi-class classification using the iris dataset. The dataset contains 150 samples for 3 different types of irises.