Treasure for Data Science blogs (A to Z)

This blog will help you in knowledge hunt of Data science. The below given list will help you to find the blogs that talk about Data science easily. I hope you will find this useful.

A Blog From a Human-engineer-being 
Aakash Japi 
Adit Deshpande 
Advanced Analytics & R 
Adventures in Data Land 
Agile Data Science 
Ahmed El Deeb 
Airbnb Data blog 
Alex Castrounis | InnoArchiTech 
Alex Perrier 
Algobeans | Data Analytics Tutorials & Experiments for the Layman 
Amazon AWS AI Blog 
Analytics Vidhya 
Analytics and Visualization in Big Data @ Sicara 
Andreas Müller 
Andrej Karpathy blog 
Andrew Brooks 
Andrey Kurenkov 
Anton Lebedevich’s Blog 
Arthur Juliani 
Audun M. Øygard 
Avi Singh 
Beautiful Data 
Becoming A Data Scientist 
Ben Bolte’s Blog 
Ben Frederickson 
Berkeley AI Research 
Big-Ish Data 
Blog on neural networks 
Blogistic RegressionAbout Projects 
blogR | R tips and tricks from a scientist 
Brain of mat kelcey 
Brilliantly wrong thoughts on science and programming 
Bugra Akyildiz 
Building Babylon 
Carl Shan 
Chris Stucchio 
Christophe Bourguignat 
Christopher Nguyen 
Cloudera Data Science Posts 
colah’s blog 
Cortana Intelligence and Machine Learning Blog 
Daniel Forsyth 
Daniel Homola 
Daniel Nee 
Data Based Inventions 
Data Blogger 
Data Labs 
Data Meets Media 
Data Miners Blog 
Data Mining Research 
Data Mining: Text Mining, Visualization and Social Media 
Data Piques 
Data School 
Data Science 101 
Data Science @ Facebook 
Data Science Insights 
Data Science Tutorials 
Data Science Vademecum 
Dataquest Blog 
David Mimno 
Dayne Batten 
Deep Learning 
Delip Rao 
District Data Labs 
Diving into data 
Domino Data Lab’s blog 
Dr. Randal S. Olson 
Drew Conway 
Dustin Tran 
Eder Santana 
Edwin Chen 
Emilio Ferrara, Ph.D. 
Entrepreneurial Geekiness 
Eric Jonas 
Eric Siegel 
Erik Bern 
Eugenio Culurciello 
Fabian Pedregosa 
Fast Forward Labs 
Florian Hartl 
Full Stack ML 
Garbled Notes 
Greg Reda 
Hyon S Chu 
i am trask 
I Quant NY 
Insight Data Science 
Ira Korshunova 
I’m a bandit 
Jason Toy 
Jeremy D. Jackson, PhD 
Jesse Steinweg-Woods 
Joe Cauteruccio 
John Myles White 
John’s Soapbox 
Jonas Degrave 
Joy Of Data 
Julia Evans 
Keeping Up With The Latest Techniques 
Kenny Bastani 
Kevin Davenport 
kevin frans 
korbonits | Math ∩ Data 
Large Scale Machine Learning 
Lazy Programmer 
Learn Analytics Here 
Learning With Data 
Life, Language, Learning 
Locke Data 
Louis Dorard 
Machine Learning (Theory) 
Machine Learning and Data Science 
Machine Learning 
Machine Learning Mastery 
Machine Learning Blogs 
Machine Learning, etc 
Machine Learning, Maths and Physics 
Machined Learnings 
MAPR Blog 
Math ∩ Programming 
Matthew Rocklin 
Melody Wolk 
Mic Farris 
Mike Tyka 
minimaxir | Max Woolf’s Blog 
Mirror Image 
Mitch Crowe 
Models are illuminating and wrong 
Moody Rd 
Mourad Mourafiq 
My thoughts on Data science, predictive analytics, Python 
Natural language processing blog 
Neil Lawrence 
NLP and Deep Learning enthusiast 
no free hunch 
Nuit Blanche 
Number 2147483647 
On Machine Intelligence 
Opiate for the masses Data is our religion. 
Pete Warden’s blog 
Plotly Blog 
Probably Overthinking It 
Publishable Stuff 
Pythonic Perambulations 
R and Data Mining 
Ramiro Gómez 
Random notes on Computer Science, Mathematics and Software Engineering 
Randy Zwitch 
RaRe Technologies 
Rinu Boney 
RNDuja Blog 
Robert Chang 
Rocket-Powered Data Science 
Sachin Joglekar’s blog 
Sean J. Taylor 
Sebastian Raschka 
Sebastian Ruder 
Sebastian’s slow blog 
SFL Scientific Blog 
Shakir’s Machine Learning Blog 
Simply Statistics 
Springboard Blog
Startup.ML Blog 
Statistical Modeling, Causal Inference, and Social Science 
Stigler Diet 
Stitch Fix Tech Blog 
Storytelling with Statistics on Quora 
Subconscious Musings 
Swan Intelligence 
The Angry Statistician 
The Clever Machine 
The Data Camp Blog 
The Data Incubator 
The Data Science Lab 
The Science of Data 
The Shape of Data 
The unofficial Google data science Blog 
Tim Dettmers 
Tombone’s Computer Vision Blog 
Tommy Blanchard 
Trevor Stephens 
Trey Causey 
UW Data Science Blog 
Wes McKinney 
While My MCMC Gently Samples 
Will do stuff for stuff 
Will wolf 
William Lyon 
Win-Vector Blog 
Yanir Seroussi 
Zac Stewart 
ℚuantitative √ourney 

Data Viz ! Cheat sheet for R Data Analyst

Data visualization has become a vital slice of data science arena. Hence, our key tool should have strong capabilities on both the fronts – data analysis as well as data visualization. With this revolution in the landscape, or has extended immense popularity because of its splendid data visualization capabilities. With a few lines of code, you can produce beautiful charts and data stories. R contains superb libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others. Below is the cheat sheet of widespread visualization for representing data. Thanks to my colleague for sharing this.

Data Viz Cheat Sheet

Introducing cricketr! : An R package to analyze performances of cricketers

A very good analysis using R in the field of cricket. Must see ! 🙂

Giga thoughts ...

Yet all experience is an arch wherethro’
Gleams that untravell’d world whose margin fades
For ever and forever when I move.
How dull it is to pause, to make an end,
To rust unburnish’d, not to shine in use!

Ulysses by Alfred Tennyson


This is an initial post in which I introduce a cricketing package ‘cricketr’ which I have created. This package was a natural culmination to my earlier posts on cricket and my completing 9 modules of Data Science Specialization, from John Hopkins University at Coursera. The thought of creating this package struck me some time back, and I have finally been able to bring this to fruition.

So here it is. My R package ‘cricketr!!!’

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package only uses data from test cricket. I plan to develop functionality for One-day and…

View original post 2,667 more words

Eight Steps to become a Data Scientist ! (The Sexiest and the Hot Job of the Decade)

Thinking how to become a Data Scientist? Here we go, the 8 Steps to become a Data Scientist (The Sexiest and the Hot Job of the Decade)

Well, these steps are not so easy but possible if we try. Most of the steps come with no-cost or very low-cost.

Thanks for DataCamp for the nice infographic. Is this info useful? Then please share this info with your circle.

Clash of the Titans ! (R vs Python)

This is to all out there who are wondering which is better language to learn for data analysis and visualization. Whether one should use R or Python when they do their everyday data analysis tasks.

Both Python and R are amongst the most extensively held languages for data analysis, and have their supporters and opponents. While Python is a lot praised for being a general-purpose language with an easy-to-understand syntax, R’s functionality is developed with statisticians in thoughts, thus giving it field-specific advantages such as excessive features for data visualization.

The DataCamp has recently released a new infographic for everyone interested in how these two (statistical) programming languages relate to each other. This superb infographic discovers what the strengths of R over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.

R vs Python for data science


Not to ignore the new entrant in war field “Julia” language. It is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming. Influenced by MATLAB, C, Python, Perl, R, Ruby and others.

Soon we expect Julia to join the clash !

Steps to Learn Data Science using R

One of the common difficulties individuals face in learning R is lack of an organized way. They don’t know, from where to start, how to proceed, which way to choose? However, there is a surplus of good free resources accessible on the Internet, this could be overwhelming as well as puzzling at the mean time.

After mining through infinite resources & archives, here is a comprehensive Learning way on R to learn R from the beginning. This will help you to learn R rapidly and proficiently.

Step 1: Download and Install R

The easy way to proceed is to download the basic version of R and installation instructions from CRAN site. R is available for Windows, Mac and Linux. Windows and Mac users most likely want one of these versions of R. R is part of many Linux distributions, you should check with your Linux package management system in addition to the link above.

You can now install various packages. There are more than 9000 packages in R for different purposes. Here is a link to understand packages called CRAN Views.  You can accordingly select the sub type of packages that you want.

To install a package you can just do this

For example, if we want to install a package called “animation” then we use


Normally the package should just install, however:

  • if you are using Linux and don’t have root access, this command won’t work.
  • you will be asked to select your local mirror, i.e. which server should you use to download the package.

You must also install RStudio. It helps R coding much easier since it allows you to type multiple lines of code, handle plots, install and maintain packages and navigate your programming environment.

Step 2: Learn the basics

You need to start by knowing the basics of the language, libraries and data structure. The R track from Datacamp is the best place to start your journey. See the free Introduction to R course at After doping this course, you would be comfortable writing basic scripts on R and also understand data analysis. Alternately, you can also see Code School for R at

If you want to learn R offline on your own time – you can use the interactive package swirl from

Primarily learn  read.table, data frames, table, summary, describe, loading and installing packages, data visualization using plot command.

Step 3: Learn Data Management:

You need to use them a lot for data cleaning, especially if you are going to work on text data. The best way is to go through the text manipulation and numerical manipulation assignments. You can learn about connecting to databases through the RODBC  package and writing sql queries to data frames through sqldf  package.

Step 4: Study specific packages in R– data.table and dplyr Here we go ! Here is a brief introduction to numerous libraries. We need to start practising some common operations.

  • Practice the data.table tutorial  thoroughly here. Print and study the cheat sheet for data.table
  • Next, you can have a look at the dplyr tutorial here.
  • For text mining, start with creating a word cloud in R and then learn learn through this series of tutorial: Part 1 and Part 2.
  • For social network analysis read through these pages.
  • Do sentiment analysis using Twitter data – check out this and this analysis.
  • For optimization through R read here and here

Step 5: Effective Data Visualization through ggplot2

  • Read Edward Tufte and his principles on how to make data visualizations here . Especially read on data-ink, lie factor and data density.
  • Read about the common pitfalls on dashboard design by Stephen Few.
  • For learning grammar of graphics and a good way to do it in R. Go through this link from Dr Hadley Wickham creator of ggplot2 and one of the most brilliant R package creators in the world today. You can download the data and slides as well.
  • Are you interested in visualzing data on spatial analsysis. Go through the amazing ggmap package.
  • Interested in making animations thorugh R. Look through these examples. Animate package will help you here.
  • Slidify will help supercharge your graphics with HTML5.

Step 6: Learn Data mining and Machine Learning Now, we come to the most valuable skill for a data scientist which is data mining and machine learning. You can see a very comprehensive set of resources on data mining in R here at . The rattle package really helps you with an easy to use Graphical User Interface (GUI).  You can see a free open source easy to understand book here at You will go through an overview of  algorithms like regressions, decision trees, ensemble modelling and   clustering.  You can also see the various machine learning options available in R by seeing the relevant CRAN view here. Resources:

Step 7: Practice Practice with example data available with you and on the internet. Stay in touch with what your fellow R coders are doing by subscribing to , and Go through the questions and answers that users come up with. Start interacting by asking questions and providing the answers for the questions which you can ! Happy learning !!! 🙂