Data Scientist

Data Scientist articles

18 Free Exploratory Data Analysis Tools For People who don't code so well. Introduction Some of these tools are even better than programming (R, Python, SAS) tools.

All of us are born with special talents. It’s just a matter of time until we discover it and start believing in ourselves. We all have limitations, but should we stop there? No. When I started coding in R, I struggled. Now when I look back, I laugh at myself. Data exploration is an inevitable part of predictive modeling. I have written this article to help you acknowledge various free tools available for exploratory data analysis. List of Non Programming Tools 1. If you are transitioning into data science or have already survived for years, you would know, even after countless years, excel remains an indispensable part of analytics industry.

It supports all the important features like summarizing data, visualizing data, data wrangling etc. which are powerful enough to inspect data from all possible angles. Free Download: Click Here 2. Free Download: Click Here 3. Most Active Data Scientists, Free Books, Notebooks & Tutorials on Github. Introduction “Who’s your favorite data scientist?”

Asked the recruiter. None of the candidates could give a satisfactory answer. May be, they thought becoming a data scientist has nothing to do with following them. What You're Doing Is Rather Desperate. At any R Q&A site, you’ll frequently see an exchange like this one: Q: How can I use a loop to […insert task here…] ?

A: Don’t. Use one of the apply functions. So, what are these wondrous apply functions and how do they work? I think the best way to figure out anything in R is to learn by experimentation, using embarrassingly trivial data and functions. Let’s examine each of those. 1. applyDescription: “Returns a vector or array or list of values obtained by applying a function to margins of an array or matrix.” OK – we know about vectors/arrays and functions, but what are these “margins”?

That last example was rather trivial; you could just as easily do “m[, 1:2]/2” – but you get the idea. 2. by Updated 27/2/14: note that the original example in this section no longer works; use colMeans now instead of mean.Description: “Function ‘by’ is an object-oriented wrapper for ‘tapply’ applied to data frames.” The by function is a little more complex than that.

The replicate function is very useful. Shiny - Tutorial. The How to Start Shiny video series will take you from R programmer to Shiny developer.

Watch the complete tutorial here, or jump to a specific chapter by clicking a link below. The entire tutorial is two hours and 25 minutes long. Part 1 - How to build a Shiny app Part 3 - How to customize appearance You will get the most out of these tutorials if you already know how to program in R, but not Shiny. If R is new to you, you may want to check out the learning resources at www.rstudio.com/training before taking one of these tutorials. If you use Shiny on a regular basis, you may want to skip these tutorials and visit the articles section of the Development Center.

Shiny JavaScript Tutorial Herman Sontrop and Erwin Schuijtvlot of FRISS are writing a series of lessons that will teach you how to create custom JavaScript widgets and embed them into your Shiny apps. Learn R. Learn Git Branching. Abbass-al-sharif. In-depth introduction to machine learning in 15 hours of expert videos. In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).

I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. ISLR Fourth Printing. Short-refcard.pdf. Untitled. Useful new R packages for data visualization and analysis. The following is from a hands-on session I led at the recent Computer Assisted Reporting conference.

There's a lot of activity going on with R packages now because of a new R development package called htmlwidgets, making it easy for people to write R wrappers for existing JavaScript libraries. The first html-widget-inspired package I want us to demo is Leaflet for R. If you’re not familiar with Leaflet, it’s a JavaScript mapping package. To install it, you need to use the devtools package and get it from GitHub (if you don't already have devtools installed on your system, download and install it with install.packages("devtools"). devtools::install_github("rstudio/leaflet") Load the library library("leaflet") Step 1: Create a basic map object and add tiles mymap <- leaflet() mymap <- addTiles(mymap) View the empty map by typing the object name: mymap Step 2: Set where you want the map to be centered and its zoom level mymap <- setView(mymap, -84.3847, 33.7613, zoom = 17) mymap Add a pop-up.

Learn R for beginners with our PDF. With so much emphasis on getting insight from data these days, it's no wonder that R is rapidly rising in popularity.

R was designed from day one to handle statistics and data visualization, it's highly extensible with many new packages aimed at solving real-world problems and it's open source (read "free"). If you're ready to learn, we have just the ticket: A free PDF of Computerworld's "Beginner's guide to R. " Included in this 45-page guide: Introduction: First steps, including downloading R and RStudio, setting your working directory and installing and using packages. r4beginners_v3.pdf. R Sites.