statistics-for-data-science

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

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

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.

NASA Bearing Dataset: Fault Detection with Wiener denoising and custom time-frequency btstft Transforms

The complete, structured curriculum repository for aspiring Data Analysts. Contains essential Python notebooks, practical exercises, and public datasets covering core data analysis, visualization, and reporting techniques.

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

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.

Shiny App for regression analysis using GAMLSS

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.

Applied Statistical Modelling course modules, practical reports, Python notebooks, and empirical datasets.

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.

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

End-to-end data science project predicting SME credit card churn using statistical testing, ML, and causal inference. Identifies $2.5M+ revenue at risk with interactive Tableau dashboard and 16 SQL queries.

A collection of Python learning materials, practical examples, and exercises for Data Science, Machine Learning, and Artificial Intelligence.

Shiny App for GLMM using R to provide similar outputs as SAS