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100dayscodechallenge

A repository for 100 days of Code Challenge

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

arrow

Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication. Languages currently supported include C, C++, Java, JavaScript, Python, and Ruby.

chatgpt-equations

Visualize equations using Latex notation inside ChatGPT

code

Example application code for the python architecture book

compose

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.

country_converter

The country converter (coco) - a Python package for converting country names between different classification schemes.

covid_interventions

See ReadMe for details!

craigslist-cars-for-sale-data-project

craigslist-vehicle-data-analysis

Analyzing the Craigslist Vehicle dataset is a comprehensive task, and the depth of your analysis can vary depending on your specific goals. Below, I'll provide an overview of steps you can take to analyze this dataset: Data Exploration and Preparation:

credit-card-default

Analysis of Credit Card Default Dataset of Taiwan for Machine Learning

data-engineering-zoomcamp

Free Data Engineering course!

data-science-complete-tutorial

For extensive instructor led learning

data-science-for-everyone

Data Science boot camp aims to make the field of data science accessible and understandable to a wide range of individuals, regardless of their background or expertise.

data-science-interview-resources

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

data-science-materials

Some materials for those in data science

data-science-projects

Collection of data science projects in Python

data_science_bootcamp

datasets

didactic-guacamole

Just for Git & GitHub Practs

docs

The open-source repo for docs.github.com

dplyr

dplyr: A grammar of data manipulation

echarts

Apache ECharts is a powerful, interactive charting and data visualization library for browser

faster

Fast Lane to Learning R!

free-programming-books

:books: Freely available programming books

gitex

Learnings on Git & Github

gitexercise

Git Exercise

go

The Open Source Data Science Masters

gt-package

This is a package is a powerful package that lets you make better formatted tables for visuals. it also lets you make better labels and colors to make it easier for you and others to see tables. It starts with tibbles and lets you transform it to gt tables. This package can be used in a lot of ways. this is because a lot of fields need tables. Whether this be for stocks, medical, or the statistical field. A lot of people need to be able to see and comprehend tables and this package lets you make them more accessible. One of the most used functions is pretty simple and it is the gt() function. This function is simple and it transforms the tibble into a gt table. It is demonstrated below.

gtpackage

This is a package is a powerful package that lets you make better formatted tables for visuals. it also lets you make better labels and colors to make it easier for you and others to see tables. It starts with tibbles and lets you transform it to gt tables. This package can be used in a lot of ways. this is because a lot of fields need tables. Whether this be for stocks, medical, or the statistical field. A lot of people need to be able to see and comprehend tables and this package lets you make them more accessible. One of the most used functions is pretty simple and it is the gt() function. This function is simple and it transforms the tibble into a gt table. It is demonstrated below.