I think programming languages are called languages for a reason - and I think we all have a native and secondary language

Here's a handy lexicon between R and Python of sorts for your reference. It's sure to be handy, no matter which one is your native language!

🌎 https://lnkd.in/eG-Grrr


#datascience #dataanalysis #python #r

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R in Pharmacy? Honestly, I started losing any hope to see the pharmacy turning (even slowly!) towards R. A little has been happening since 1976, when the S (father of R), was born. And even when S, then R, gained the status of an industry standard in widely understood bio-sciences, it has never happened in pharmacy, namely in clinical research. This was -and still is- the kingdom exclusively reigned by SAS The King (with a low % of "supporters", including R).

It was a big shame, but, what could have been done against long years of spread myths, doubt, uncertainty and negative attitude?

Well, this is not that everything was right about R! Serious topics still have to be addressed, including:
1) numerical validation (ideally free, coordinated by, say, R Consortium),
2) support for CDISC-related processes,
3) metadata layer (SAS format/informat),
There are more topics, yet there's no place for details.

And then, about 5 years ago, something started changing. Slowly. More and more top-pharma companies (even FDA!) started talking about their use of R publicly, some even contributed (e.g. Merck's gsDesign tool).

Today I'd like to share with you the news: a new initiative by R Consortium - the "R in Pharma" project. http://rinpharma.com/

#R #statistics

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The ability to pull/extract data from a website is invaluable in #DataScience. Learn how to collect your own data using #WebScraping in both #Python and #R:

Beginner’s Guide on Web Scraping in R (using rvest) - https://lnkd.in/fFzU2kw

Beginner’s guide to Web Scraping in Python (using BeautifulSoup) - https://lnkd.in/fxTKYdA

Web Scraping in Python using Scrapy - https://lnkd.in/fUD_aCi

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#LogisticRegression is the most commonly used classification #algorithm in the industry. Here are 3 articles to understand the nitty-gritty of this technique:

Simple Guide to Logistic Regression in #R - https://lnkd.in/fQHsskA

Building a Logistic Regression model from scratch - https://lnkd.in/fK79Nf5

How to use Multinomial and Ordinal Logistic Regression in R? - https://lnkd.in/fHFHnDq

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Here's a cheatsheet on Scikit-Learn (machine learning library that provides a range of supervised & unsupervised algorithms in #Python) and Caret package (used for solving any supervised machine learning problem in #R) we would like to share with you. #ScikitLearn #Caret https://lnkd.in/fgfR3FU

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Getting started with #datascience and #machinelearning? Don't miss out on these 5 incredible articles covering various #ML algorithms (+ code) every beginner must know:

6 Easy Steps to Learn #NaiveBayes #Algorithm (with codes in #Python and #R) - https://lnkd.in/fVz5sS5

Introduction to k-Nearest Neighbors: Simplified - https://lnkd.in/fghna-N

Understanding Support Vector Machine algorithm from examples - https://lnkd.in/fW8AhpS

A comprehensive beginner’s guide to create a Time Series Forecast - https://lnkd.in/f7ZAVPE

Essentials of Machine Learning Algorithms -
https://lnkd.in/fdEGhjf

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Visualizations are one of the best ways of telling a story with data. They are extremely useful when trying to understand data and unearth hidden patterns. Check out these 4 articles to design mind-blowing visualizations:

A Collection of 10 Data Visualizations You Must See - https://lnkd.in/fRemdbn

How to create Beautiful, Interactive data visualizations using Plotly in #R and #Python - https://lnkd.in/fN3e9m8

Comprehensive Guide to #DataVisualization in R - https://lnkd.in/fw9M-De

R-analyst #Cheatsheet: Data Visualization in R - https://lnkd.in/fnakeqH

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One of my favorite tricks is adding a constant to each of the independent variables in a regression so as to shift the intercept. Of course just shifting the data will not change R-squared, slopes, F-scores, P-values, etc., so why do it?

Because just about any software package capable of doing regression, even Excel, can give you standard errors and confidence intervals for the Intercept, but it is much harder to get most packages to give you standard errors and confidence intervals around the predicted value of the dependent variable for OTHER combinations of the independent variables. Shifting the intercept is an easy way to get confidence intervals for arbitrary combinations of the independent variables.

This sort of thing becomes especially important at a time when the Statistics community is loudly calling for a move away from P-values. Instead it is recommended that researchers give confidence intervals in clinically meaningful terms.
#data #researchers #statistics #r #excel #regression

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All Data Science ***Cheat Sheets*** in one place.


Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R

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All ***Cheat Sheets*** in one place.

Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R

✴️ @AI_Python_EN