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

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

#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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN

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

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

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

✴️ @AI_Python_EN

An overview for using #R for validated work:

1.) Base R #Validation for #FDA: https://lnkd.in/ep8TRM8

2.) #RStudio IDE Validation: https://lnkd.in/e34FCXn

3.) Evaluating Package Stability

4.) Evaluating Package Dependencies: https://lnkd.in/eniCXgG

5.) Organizing Packages with an Internal Repository: https://lnkd.in/etSGuk4

#rstats

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Which is the best tool amongst #Python, #R and #SAS for the job? If you are also looking for an answer, then this Infographic is what you should follow. https://lnkd.in/frqar5E

✴️ @AI_Python_EN

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

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

#datascience #machinelearning #excel #deeplearning #python #R #sql #matlab #datamining #datawarehousing

✴️ @AI_Python_EN

The ability to deal with imbalanced datasets is a must-have for any #datascientist. Here are 4 tutorials to learn the different techniques of handling imbalanced data:

How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M

Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1

This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0

Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr

✴️ @AI_Python_EN

The best way to learn #DeepLearning is by practicing it. But which framework to use? Here are 5 articles to get you started!

A Comprehensive Introduction to #PyTorch - https://bit.ly/2L8Rj7n

Learn How to Build Quick & Accurate Neural Networks using PyTorch (& 4 Case Studies) - https://bit.ly/2Vts9nY

Get Started with Deep Learning using #Keras and #TensorFlow in #R - https://bit.ly/2Iro2BY

TensorFlow 101: Understanding Tensors and Graphs - https://bit.ly/2GNg195

An Introduction to Implementing #NeuralNetworks using TensorFlow - https://bit.ly/2V17cBs

✴️ @AI_Python_EN

Data Visualization is a very important step in Data Science, so we should try to MASTER it.

Here are the useful links for Data Visualization -

1)Quick and Easy Data Visualizations in Python with Code.
(https://lnkd.in/fXJ-_Y8)

2)10 Useful Python Data Visualization Libraries for Any Discipline.
(https://lnkd.in/fBxbHwr)

3)Top 50 matplotlib Visualizations – The Master Plots (with full python code).
(https://lnkd.in/fGrnGax)

4)Data Visualization Effectiveness Profile.
(https://lnkd.in/f3v52Fd)

5)The Visual Perception of Variation in Data Displays.
(https://lnkd.in/fm-TbPM)

6)Matplotlib Tutorial – A Complete Guide to Python Plot w/ Examples.
(https://lnkd.in/fFkUgQP)

7)Interactive Data Visualization in Python With Bokeh.
(https://lnkd.in/fEfQAvg)

8) Data Visualization in R
https://lnkd.in/fEvZB_N

9) The Next Level of Data Visualization in Python (Plotly)
https://lnkd.in/fKn4cPM

#datascience #dataanalysis #datavisualization #python #r

✴️ @AI_Python_EN

letting beginners and experts alike learn about SAP HANA.
Download here --> https://lnkd.in/eTtdvi4

End to end Machine learning platform.
Bring your own language and microservices.Java, Node.js and Python are the officially supported languages.

SAP HANA is an ACID-compliant database and application development platform. You can use advanced data processing capabilities—text, graph, spatial, predictive, and more—to pull insights from all types of data.

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #python #R #java #SQL

✴️ @AI_Python_EN

All You Need About Common #MachineLearning Algorithms

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost


#ai #datascienece

✴️ @AI_Python_EN

💡💡 Commonly used Machine Learning Algorithms 💡💡

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost

Credit: Analytics Vidhya,Sunil Ray

Thanks for the share Steve Nouri.

#datascience #deeplearning #ai #artificialintelligence #machinelearning #data #r #python

✴️ @AI_Python_EN

Quick links for all things #R and #Python:

1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ

For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG

✴️ @AI_Python_EN

Credit Risk Analysis Using #MachineLearning and #DeepLearning Models

Lovely paper by Peter Martey Addo, Dominique Guegan and Bertrand Hassani

Code on #Github (it's in #R)

https://github.com/brainy749/CreditRiskPaper

✴️ @AI_Python_EN