A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python

Deep R Programming (Open-Access Textbook)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. Learn statistical concepts that are very important to Data science domain and its application using Python. Learn about Numpy, Pandas Data Frame.

This repository contains a gentle introduction to machine learning algorithms with hands on practical examples

This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets.

Learn the core statistical concepts, followed by application of these concepts using R Studio with the a nice combination of theory and practice. Learn key statistical concepts and techniques like exploratory data analysis, correlation, regression, and inference.

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

The Following problems showcase different Statistical Methods used for Decision Making. The purpose of this project is to experiment and execute statistical methods, which are required to conduct data analysis, derive insights and inferences and arrive at business decisions.

collection of Jupyter Notebooks in both English and Spanish, dedicated to performing data quality analysis using the R programming language

This portfolio features all the Data Science and Machine Learning projects I have completed for academic, self-learning and hobby purposes. Additionally, it is updated regularly.

Hypothesis Testing in Data Analysis This repository contains a Jupyter Notebook demonstrating various hypothesis testing techniques using Python. It covers statistical tests such as t-tests, ANOVA, chi-square, and non-parametric methods, with real-world examples and visualizations.

This is a repository containing the notes on statistics and probability for Data Science from basics to Advance

WHO LIFE EXPECTANCY: Studying the factors that affect/contribute to life expectancy and analyzing the changes over the last 15years, that is between 2000-2015.

Using boxplots to investigate US hospitals healthcare costs

The Poisson Distribution models the number of events that occur within a specified time frame, such as years. Since the volume of incoming calls fluctuates from year to year, this distribution aids in determining whether the call data aligns with a Poisson process or if external factors are affecting the call volume.

Project of data analytics

This repository includes all the assignments completed for the IDS702: Modelling & Representation of Data at Duke MIDS program.

This repo contains all two problem set solutions of Applied Regression Course.

The Poisson distribution is a useful model for analyzing product defects, helping to estimate expected defect rates, their variability, and the likelihood of extreme cases. This understanding aids in enhancing quality control processes and minimizing defects.