Summary blogdown: Creating Websites with R Markdown provides a practical guide for creating websites using the blogdown package in R. In this book, we show you how to use dynamic R Markdown documents to build static websites featuring R code (or other programming languages) with automatically rendered output such as graphics, tables, analysis results, and HTML widgets. The blogdown package is also suitable for technical writing with elements such as citations, footnotes, and LaTeX math. This makes blogdown an ideal platform for any website designed to communicate information about data science, data analysis, data visualization, or R programming. Note that blogdown is not just for blogging or sites about R; it can also be used to create general-purpose websites. By default, blogdown uses Hugo, a popular open-source static website generator, which provides a fast and flexible way to build your site content to be shared online. Other website generators like Jekyll and Hexo are also supported. In this book, you will learn how to: Build a website using the blogdown package; Create blog posts and other website content as dynamic documents that can be easily edited and updated; Customize Hugo templates to suit your site’s needs; Publish your website online; Migrate your existing websites to blogdown and Hugo.I like to analyze data to answer research questions and test hypotheses. Currently I investigate questions related to breast cancer through my work as a Research Biostatistician at [Memorial Sloan Kettering Cancer Center](https://www.mskcc.org/departments/epidemiology-biostatistics) in the department of Epidemiology & Biostatistics.

awesome-python-for-data-science

A curated list of awesome resources such as books, tutorials, courses, open-source libraries, exercises, and other materials that support Pythonistas in the making, and Pythonistas migrating into Data Science! 📊

basic-python-rough-works-

Learning Python basics is a piece of cake, it is extremely simple to get up and running with Python. Basics like variables, operators and control structures are extremely easy to learn as opposed to other languages like Java.Its been 15 days that I have started Python learning so I feel that I am eligible to answer this question , I am person who doesn't have any prior programming experience. But my job profile pushing me to learn Python and that's it, I have browse internet and I got so many sources free as well as paid but I was confused which source should I follow whether youtube, or some website , but finally I got one PDF which has 250 pages , it's very good I will not say it's great but for starting point it's very good, to know the Python's fundamental, once you complete this 250 pages then you can move to the other sources by the time you will have more clarity in Python . If you need I may send you this to your mail id.

basics-of-python-rough-practices-

I fell in love with Python after reading a bunch of answers on Quora about how people were doing wonderful things with Python. Some were writing scripts to automate their Whats app messages. Some wrote a script to download their favourite songs, while some built a system to receive cricket score updates on their phones. All of this seemed very excited to me and I finally decided that I would love to learn Python.

chatbot

Created a chatbot with voice output using tflearn.

dashboards-dossiers-reports

data-management-with-r-python-sql-sas

Cleaning data will take 40% of your time. I showed solutions to frequent problems in common platforms. Also discussed web scraping and text mining.

data-science--cheat-sheet

Cheat Sheets

data-science-interview-preparation

decision-tree---siva-k

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Decision Tree algorithms are referred to as CART (Classification and Regression Trees).

django-simple-bulma

Django application to add the Bulma CSS framework and its extensions

energhelpline-webscraping

Web scraping is nothing but collecting data from various websites. You can extract information, such as product pricing and discounts. The data that you acquire can help in enhancing user experience. This usage, in return, will ensure that the customers prefer you over your competitors.

fivem

The source code for the FiveM modification framework for GTA V.

flask-boilerplate

Boilerplate template for a Python Flask application with Flask-SQLAlchemy, Flask-WTF, Fabric, Coverage, and Bootstrap

flipkart-scrapping-

flipkart-web-scraping-using-python

Basically it contains code and procedures of how we can scrap any web and get tons of information. It is easy to understand.

forecasting-techniques-with-some-examples

Start with README

hapijs-mongoose-restapi

a RESTful API using hapijs and mongoose

httrack

HTTrack Website Copier, copy websites to your computer (Official repository)

ibm-spss-modeler-c2090-930-preparation-18-v3-

First of all u need a IBM Exam Registration :

knn---assignment-

Calculate the distance from x to all points in your data. Sort the points in your data by increasing distance from x. Predict the majority label of the k closest points. Note that the value of k effects the results, its ideal to test the model for different values of k for better results and there by a better model.

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