101 DATA SCIENCE with Cheat Sheets (ML, DL, Scraping, Python, R, SQL, Maths & Statistics) by Anushka Bajpai via @Medium

Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That’s why we have cheat sheets and summaries. They help us access the most commonly needed reminders for making our Data Science journey fast and easy.

This is really like a one-stop-shop for cheatsheets - definitely worth a bookmark, a printout, adding to Evernote or whatever is your choice for preserving something important.

A lightweight data validation ecosystem with R, GitHub, and Slack by/via @EmilyRiederer

Nice approach for building an "advanced" but right-size data monitoring solution using common tools: GitHub (Actions, Pages, issues), R (pointblank + projmgr pkgs), and Slack notifications.

This is very useful and definitely worth a bookmark if not at printout or an addition to a folder in Evernote.

R vs. Python vs. Julia by @daniel_c_moura via @TDataScience

How easy it is to write efficient code?

I found this really insightful and made me think a little more about any code I write.

WEBINAR: Developing and Testing Shiny Apps - 12 March 2020

Data Science Central Webinar Series Event

Developing and Testing Shiny Apps Join us for the latest DSC Webinar on March 12th, 2020 Shiny is the most popular framework among R users for developing dashboards and web applications. It is commonly used by statisticians and data scientists to present and share their work with broader groups. These dashboards are often developed inside the RStudio IDE and then published to hosting servers. RStudio IDE users have been enjoying the power of Databricks clusters and other workspace features since 2018. Now they can use Shiny on Databricks as well. In this latest Data Science Central webinar, we will review how RStudio Server works on Databricks clusters and the advantages of running RStudio Server inside the Unified Data Analytics Platform. We will introduce a new addition to the Unified Platform for R users on Databricks: support for Shiny applications. This webinar will include a demo that will focus on the lifecycle of developing and testing Shiny applications inside hosted RStudio Server, as well as what can be done with a high-bandwidth connection to a powerful Apache Spark cluster. Speaker: Hossein Falaki, Tech Lead -- Databricks Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Developing and Testing Shiny Apps Date:  Thursday, March 12th, 2020 Time:  09:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

What’s next for the popular programming language R? by Dan Kopf via @qz

Hadley Wickham discusses R and where it’s going, tidy evaluation, and the different cultures of R and Python users and how their viewpoints differ.

Any R user will find this really interesting. I have to agree with him that there is no "competition" between R and Python - it's more about what is best for what you are doing, what you are comfortable using or a combination of the two. I like R and I am better at R than Python (which I struggle with at times). That's just me, and I'm sure others are the other way around.

3 strategies for working with data in R by Alex Gold via @rstudio

"For many R users, it’s obvious why you’d want to use R with big data, but not so obvious how." Here's how.

Loved this well thought out article. Definitely worth a bookmark to save it somewhere for later/next time.

How the BBC Visual and Data Journalism team works with graphics in R by BBC Visual and Data Journalism via @Medium

This is an interesting look at how the BBC uses R’s ggplot2 package to create production-ready charts.

Who knew this was behind what we see on the screen. Interesting read.

WEBINAR: Creating Business Applications With R & Python - 12 February 2019

Creating Business Applications With R & Python Join us for the latest DSC Webinar on February 12th, 2019 Across industries, data scientists are creating powerful models and analytics to solve urgent business problems. However, in far too many cases, these analytics never reach their intended business users. The result is wasted time and effort, as well as a failure to achieve the fundamental goal of transforming data and analytics into solutions. Please join this latest Data Science Central webinar to see how data science teams can stop this trend and start putting analytics into action. With FICO® Xpress Insight, it's easy to take any advanced analytic asset (such as an R or Python script) and turn it into a fully functioning application for business users. We'll demonstrate some key features, including: An environment that fosters collaboration between data scientists and business users during model creation A robust interface for rapidly deploying validated models into business user-friendly applications Enablement tools for business users to run models, perform simulations, compare scenarios and visualize outcomes Data scientists can finally stop seeing their efforts go to waste and start empowering business users with the predictive and prescriptive analytics capable of transforming businesses–join us to learn more! Speakers: Bill Doyle, VP of Decision Management Solutions -- FICO Libin Varghese, Principal Sales Consultant, Decision Management Solutions -- FICO Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Creating Business Applications With R & Python Date:  Tuesday, February 12th, 2019 Time:  9 AM - 10 AM PST

Register here

What Is a Data Frame? (In Python, R, and SQL) by/via @oilshellblog

This post introduces data frames and shows how they work by solving the same problem three ways: without data frames, with data frames in Python and R, and in plain SQL.

I love this which allows you to compare and contrast the method across all three so that you can see the idea is the same but the implementation is different. Definitely worth a bookmark.

WEBINAR: Getting Data Down to a Science – Code-free and Code-friendly ML - 5th September 2018

Data Science helps answer some of the most basic - and the most complex - business questions. In this latest Data Science Central webinar you will learn how to get data down to a science with code-free and code-friendly self-service analytics platforms. Decisive Data’s Lead Data Scientist Tessa Jones will use a sample data set from a global corporation to answer some of the most common data science questions applicable across businesses. Learn how to use code-free and code-friendly Machine Learning: Dive – Swim in the data and dive into a few common business questions with answers in data science including demand forecasting and customer segmentation. Build – Walk through two data science models including code-free time series and clustering machine learning models. Customize – Implement custom R code into models. Refine – Enhance your methods with rapid self-service techniques. Display – Creatively display information visually in Tableau and tell a story that makes the findings clear and captivating using the Art + Data methodology. Speakers: Tessa Jones, Lead Data Scientist -- Decisive Data Scott Trauthen, Director of Marketing -- Alteryx Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Getting Data Down to a Science – Code-free and Code-friendly Machine Learning Date:  Wednesday, September 5th, 2018 Time:  9 AM - 10 AM PDT

Join here

Top 20 R Libraries for Data Science in 2018 by/via @activewizards

An infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.

This is a great infographic and so useful I think if you are likely to do anything in R you should bookmark it as well as print out a copy for you to write a few notes on.

Top Big Data Skills To Help You Stand Out from the Crowd by Sarah Shannon via SmartDataCollective

Big Data is the latest buzzword hitting the technology sector with data analytics fast becoming the newest technique implemented by businesses to monitor their IT networks, and stop impending threats.

Definitely something to read and work out which skills you might be missing or would add to your offering if you worked on it.

Create editable Microsoft Office charts from R by David Smith via @rbloggers

Embedding an R graphic into a Microsoft Office Document can easily be done. But can you make it editable in Microsoft Office? This post introduces two packages that allow you to do this.

This is a great article and could help you even if you didn't want to embed it into MS Office.

The Python and R Graph Gallery by/via @R_Graph_Gallery

This could be handy for your next Python or R data viz project: hundreds of charts along with the reproducible R and Python code.

Something to bookmark and keep for the next time you need to find a a great chart for your data.

Introducing R-Brain: A New Data Science Platform by @idigdata via @kdnuggets

R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

Great article and it sounds like a great platform. I'm hoping to have a go with it next week if I can find the time to play.

How can R Users Learn Python for Data Science? by @Manish_Saraswt via @HackerEarth

Python is a supremely powerful and a multi-purpose programming language. It has grown phenomenally in the last few years. It is used for web development, game development, and now data analysis / machine learning. Data analysis and machine learning is a relatively new branch in python. For a beginner in data science, learning python for data analysis can be really painful.

This blog entry is really interesting and is perfect if you know R but want an easy intro to Python as it gives you the relevant translation.

Python overtakes R, becomes the leader in Data Science, Machine Learning platforms by Gregory Piatetsky via @kdnuggets

"Python did not quite 'swallow' R, but the results, based on 954 voters, show that in 2017 the Python ecosystem overtook R as the leading platform for analytics, data science, and machine learning."

Wow - maybe I need to focus on Python more than R now (even though I much prefer R).

The Guerrilla Guide to Machine Learning with R by Matthew Mayo via @kdnuggets

This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.

This is a great article and includes many instructional videos and links to find out more.

Data validation with the assertr package by/via @tonyfischetti

The assertr package has some wonderful validation constructs: even if you don’t spend a lot of time in R, it’s worth reading this piece purely for its approach to scale-able data validation.

The code examples and the comments with replies are a must read too as I think if you add the article, the examples and the questions it adds to the complete value in looking at this.  Definitely worth a look for anyone who uses R but can also give some overall help for anyone who validates data.

WEBINAR: Data Science Made Simple with SPSS - 7 March 2017

Overview

Title: Data Science Made Simple with SPSS

Date: Tuesday, March 07, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Data Science Made Simple with SPSS  

For decades, IBM SPSS® Statistics has been the trusted data analytics package for statisticians, researchers, and business analysts. That’s because it offers superior capabilities, flexibility and usability that are not available in traditional statistical software. Now, IBM SPSS Statistics is available by subscription, offering even greater speed and ease of use than ever before—with no more software licenses or worrying about version updates.

Join us for an overview of the new IBM SPSS Statistics Subscription. In this Data Science Central webinar, learn how you can start enjoying the benefits of a powerful, affordable data analysis tool that can help you more easily:
 

• Access, manage, and analyse virtually any kind of data set
• Gain reliable results with a broad range of tests and procedures    
• Use R and Python to further extend your capabilities   

Whether you are a beginner or an experienced analyst or statistician, IBM SPSS Statistics Subscription software puts the power of advanced statistical analysis at your fingertips. Register for this Data Science Central webinar to learn how you can start getting faster, more accurate results from your data today.

Speaker: Douglas Stauber, Offering Manager - IBM SPSS Statistics 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Register here